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Land Use Policy 33 (2013) 54– 60

Contents lists available at SciVerse ScienceDirect

Land Use Policy

jou rn al h om epa ge: www.elsev ier .com/ locate / landusepol

istributed land use modeling and sensitivity analysis for REDD+

eli Lua,b,∗, Guifang Liua

Institute of Natural Resources and Environmental Science, and College of Environment and Planning, Henan University, Kaifeng 475004, ChinaUnited Nations University - Institute of Advanced Studies, Yokohama 220-8502, Japan

r t i c l e i n f o

rticle history:eceived 25 August 2011eceived in revised form 5 December 2012ccepted 8 December 2012

eywords:and use modelingEDD+ensitivity analysis

a b s t r a c t

There is an urgent need to develop a framework for understanding and predicting the effect of opportunitycosts of REDD+. We develop an approach comprising two components: distributed land use modelingfor assessing the profitability gap between maintaining palm oil plantations and complying with REDD+and a sensitivity analysis of the model’s predictions. First, a spatially explicit model is used to predict thefuture distribution of land use changes in central Kalimantan, Indonesia. This model predicts the changein carbon storage due to deforestation by linking business-as-usual baseline emissions scenario to historicdata and using an improved cellular automaton system to predict land use changes. Input parametersinclude elevation, slope, aspect, soil types, distance to road, distance to river, etc. The so-called “ton-year approach” is combined with the future price of carbon to estimate compensation under the REDD+mechanism. Potential revenues from palm oil plantation are calculated by multiplying yields from palmoil products with corresponding prices in the world market. Second, a sensitivity analysis is conducted toassess the robustness of the modeling results to alternative assumptions about palm oil price and carbon

price. The palm oil price is shown to have the highest relative sensitivity. Further analysis indicatesremarkable changes in the profitability gap depending on the price of palm oil; a change in palm oil pricefrom $545.33 to $773.03 shows a large 155% increase in the profitability gap. Unfortunately, the mostlikely forecasts of palm oil prices continue to predict large differences in the profitability gap, favoringpalm oil plantation over REDD+ projects. Thus, the effect of carbon pricing policies, as they currentlystand, will remain limited.

© 2012 Elsevier Ltd. All rights reserved.

ntroduction

Tropical forests are known to play an important role in thelobal carbon budget because they contain about as much carbonn their vegetation and soils as do the temperate and boreal forestsombined (Melillo et al., 1993; Dixon et al., 1994; Field et al., 1998).ecent estimates suggest that the carbon released from deforesta-ion activities in the tropical region accounts for approximately5–17% of anthropogenic emissions of carbon dioxide (CO2) everyear (IPCC, 2007; Van der Werf et al., 2009). However, carboneleases attributed to deforestation activities are not addressedn the Kyoto Protocol, which is regarded as a first step towards

truly global emissions reduction regime that would stabilize

reenhouse gas (GHG) concentrations (UNFCCC, 2010). Growinglobal awareness of this issue has led to an increased focus onhe role of tropical forests in carbon budgeting under the United

∗ Corresponding author at: Institute of Natural Resources and Environmental Sci-nce, and College of Environment and Planning, Henan University, Kaifeng 475004,hina. Tel.: +86 378 2858363.

E-mail addresses: cluheli@gmail.com (H. Lu), kf guif@163.com (G. Liu).

264-8377/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.landusepol.2012.12.008

Nations Framework Convention on Climate Change (UNFCCC).During the fifteenth session of the Conference of the Parties (COP15) in December 2009, the Parties agreed that reducing emissionsfrom deforestation and forest degradation (REDD) coupled withconservation, sustainable management of forests, and enhance-ment of forest carbon stocks (denoted together as “REDD+”)in developing countries, through positive incentives under theUNFCCC, was a way of dealing with global GHG emissions.

However, proponents of REDD+ are facing a big challenge due tothe booming demand for biofuels, which are regarded as an envi-ronmentally sustainable solution to the global energy crisis anda way to counterbalance global increases in CO2 emissions. Suchdemand, especially for palm oil, appears to be driven by severalfactors: (1) the high cost of petroleum; (2) the ability to easilysubstitute palm oil for some biofuels and renewable; (3) effortsof food manufacturers in the United States to reduce the contentof trans fats in their products using soy oil; (4) and the expansiveeconomic growth in China and India, necessitating the need for

palm oil (WWF-Indonesia, 2008). The formidable combination ofimproved agricultural technologies, enabling tenure and taxationpolicies, easy access to land (Cattaneo, 2007; Hecht, 2005; Mortonet al., 2006), and the rising demand for biofuel feedstock, are said

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H. Lu, G. Liu / Land

o have accelerated deforestation at the expense of forest carbon,ative habitat, and forest biodiversity (Righelato and Spracklen,007; Koizumi and Ohga, 2007).

Like payments for environmental services (PES) (Angelsen andertz-Kanounniko, 2008; Angelsen, 2009), one of the key features

f REDD+ is voluntary participation. PES mechanisms are designedo include schemes incorporating direct checks and balances onelfare and equity. The payment must be at least equal to the min-

mum willingness-to-accept of local communities or land users,easured by its opportunity cost (Bond et al., 2009; Wunder, 2009).

he estimation of opportunity costs is important for two main rea-ons: to calculate fair compensation to land users for switching toorestry uses and to support low cost emission reduction strate-ies (Pirard, 2008). There is, thus, an urgent need to develop aramework for understanding and predicting the effect of oppor-unity costs of REDD+. This study uses simple assumptions thatelp to capture one of the important features of REDD+ schemes inoutheast Asia: land users’ opportunity costs associated with palmil plantation. An approach with two components was developed:istributed land use modeling for assessing the profitability gapetween palm oil plantation and REDD+, and sensitivity analysis ofhe model’s predictions.

ethods

tudy area

The central Kalimantan province of Indonesia has recorded aapid increase in areas devoted to palm oil plantation. Recentesearch shows that 763,000 ha of forest are directly threatened byuture plantations (Forest Watch, 2007). Our study area comprises7,940.75 ha (about 22.5 km long and 21.5 km wide) located in theorth of Palangka Raya in central Kalimantan. As of the early 1990s,his area was covered by heath forest and peat swamp (Governmentf Indonesia/FAO, 1996), but has undergone extensive deforesta-ion since 2000. Some researchers (Kanninen et al., 2007) classifyhe forest’s transition in this area to be in the “forest frontier” stage,

eaning that forest clearance will reach its maximum limit in theext 30 years, and large palm oil plantations are expected to usurphe land.

aseline mapping

A REDD+ “baseline” is defined as expected or business-as-usualBAU) emissions of CO2e (GHGs measured as equivalent units ofO2) from deforestation and forest degradation in the absence ofdditional efforts to curb such emissions (Griscom et al., 2009).n this study, we linked the BAU baseline emissions scenario toistoric data. There were two main steps in baseline mapping:etermining the deforestation rate and predicting potential loca-ions of future deforestation.

For the first step, the annual rate of deforestation was estimatedsing a linear extrapolation of the historical rate. Landsat images ofhe study area in 2000, 2005, and 2009 were classified into six landse classes through the supervised classification method: denseorest, peat, sparse forest, plantation, road, and water. Conversionsf dense forest, peat, and sparse forest were included in the “defor-station” category. The historical deforestation rate was calculatedased on two land cover maps from 2000 to 2009 and using the for-ula developed by Puyravaud (2003). This formula is derived from

he compound interest law and is more intuitive than the formula

sed by the Food and Agriculture Organization or FAO (1995).

=(

1t2 − t1

)× ln

(A2

A1

), (1)

licy 33 (2013) 54– 60 55

where A1 is the forest area at the initial time t1 (year 2000) and A2is the forest area at the final time t2 (year 2009).

Then, an improved cellular automaton (ICA) system, in whichthe cell in the regular grid changes into a finite number of possiblestates according to a local interaction rule (Von Neumann, 1996;Wolfram, 1984), was utilized to predict land use changes. The CAsystem has been very successful in view of its operationality, sim-plicity, and ability to embody both logic and mathematics-basedtransition rules, thus enabling complex patterns to emerge directlyfrom the application of simple local rules. It presents a powerfulsimulation environment represented by a grid of space (raster),in which the consequences of trends and policy interventions arevisualized by means of dynamic year-by-year land use maps. In thepractical application of this study, transition possibilities dependedon the state of a cell (like forest or non-forest), and the state ofits surrounding cells (such as elevation, slope, aspect, soil type,distance to road, distance to river/village, etc.).

Carbon credits

Total carbon emissions due to the plantations, Cf,net(t), were cal-culated through changes in carbon stocks, as seen in Eq. (2). Thecomponents of this equation include (1) the initial conversion ofthe preceding vegetation into palm oil plantation, usually based onland clearing, denoted as Cf,clear(t); (2) the decay of product, slash,and elemental carbon pools, denoted as Cf,decay(t); and (3) the bal-ance of emissions and absorption during the growth cycle of the oilpalms, depending on the growth rate and management practices,denoted as Cf,regrowth(t). Thus,

Cf,net(t) = Cf,clear(t) + Cf,decay(t) + Cf,regrowth(t), (2)

where t is the year. According to the guidelines of the Intergovern-mental Panel on Climate Change (IPCC, 2006), gains in carbon (C)are always depicted with a negative (−) sign, and emissions/losses,with a positive (+) sign. The emissions are converted to CO2e bymultiplying the value by 44/12 (stoichiometric conversion betweenCO2 and C).

The distribution of carbon stocks in biomass for different foresttypes of tropical Asia (dense forest, sparse forest, or peatland) wasused to determine the forest carbon losses (IPCC, 2006; Wahyuntoet al., 2007; Slik et al., 2010). Carbon flux from the decay was derivedfrom the response curve in tropical forests (Houghton and Hackler,2001; Ramankutty et al., 2007). We adopted a palm oil allometricequation for calculating increasing carbon stocks from the growthof the palms, which is developed by measuring palm height, palmdiameter, total number of leaves, frond base biomass, and frondbiomass (Rogi, 2002; Dewi et al., 2009).

An effective REDD+ mechanism must provide continuous incen-tives for land users to maintain their forest lands. If successful,REDD+ would preserve forests during the risky development phase,much of it permanently (Chomitz et al., 2006). In order to ensurepermanence and assign liability, the compensation fund wouldhave to be paid annually for checking forest management prac-tices on carbon accumulation, rather than verifying the existenceof trees in the area and making a one-time payment. In this con-text, the so-called “ton-year approach,” which had been discussedin the IPCC Special Report on Land Use, Land-Use Change, and Forestry(Watson et al., 2000), was adopted for estimating carbon credits.

In the ton-year approach, carbon credits are directly propor-tional to the project timeframe over which carbon is sequesteredand are assessed in terms of the environmental and economic ben-efits of limited-term sequestration (MacLaren, 2000; Sedjo et al.,

2001). In other words, it should be possible to define some mea-sure of “equivalence” between temporary credits and permanentreductions that can be used to determine how temporary cred-its over different lengths of time compare in effectiveness to

5 Use Policy 33 (2013) 54– 60

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Table 1Three basic palm oil prices from 2015 to 2035.

Year Price 1 Price 2 Price 3

2015 $780.00 $709.96 $520.592016 $780.00 $709.96 $521.242017 $780.00 $709.96 $533.102018 $780.00 $709.96 $537.172019 $780.00 $709.96 $542.062020 $715.00 $709.96 $542.872021 $715.00 $709.96 $550.332022 $715.00 $709.96 $550.332023 $715.00 $709.96 $550.332024 $715.00 $709.96 $550.332025 $715.00 $709.96 $550.332026 $715.00 $709.96 $550.332027 $715.00 $709.96 $550.332028 $715.00 $709.96 $550.332029 $715.00 $709.96 $550.332030 $715.00 $709.96 $550.332031 $715.00 $709.96 $550.332032 $715.00 $709.96 $550.332033 $715.00 $709.96 $550.332034 $715.00 $709.96 $550.33

6 H. Lu, G. Liu / Land

ermanent reductions (Marshall and Kelly, 2010). The equivalenceactor (Ef), through which the climatic effect of temporal carbontorage can be converted to an equivalent amount of avoided emis-ions is the most important parameter, and ranges from 0.007 to.02 in the ton-year approach (Dobes et al., 1998; Fearnside et al.,000). This parameter could be derived from the “equivalence time”oncept (referred to as Te), which was calculated using the follow-ng steps (Moura Costa and Wilson, 2000):

Step 1. The absolute global warming potential (AGWP) of CO2 isalculated according to the following equation (IPCC, 1995):

GWP(CO2) =∫ TH

0

ax[CO2(t)dt], (3)

here TH is the time horizon, ax is the climate-related radiativeorcing due to a unit increase in atmospheric concentration of CO2,nd [CO2(t)] is the time-decaying abundance of a pulse of emittedO2 derived from the following formula (Houghton et al., 1990):

F[CO2(t)] = 0.30036e−t/6.6993

+0.34278e−t/71.109 + 0.35686e−t/815.277, (4)

here t is the year.Step 2. It can be seen that the integral in Eq. (3) is proportionate

o the cumulative radiative forcing exerted by a unit of CO2 releasedo the atmosphere. In Eq. (4), the decay curve integral is equivalento the forcing effect of approximately 55 ton-years of CO2 over a00-year period of reference timeframe set by the Kyoto Protocol.hus, Te, the length of time that CO2 must be stored as carbon iniomass or soil for it to prevent the cumulative radiative forcingffect exerted by a similar amount of CO2 during its residence in thetmosphere, can be considered as 55 years. As a result, the value off, or the ton carbon year factor, is 1/Te or 0.0182 ton CO2 emissionsvoided.

Step 3. Finally, for each ton of carbon stored for a year, the projecteceives credits for 0.0182 ton C per year. This means that the frac-ion of the full credit to be awarded can be calculated based on theatio of the project time frame to the equivalence time.

EDD+ profitability

Compensation from REDD+ for stopping deforestation involveswo factors: carbon credits and carbon price ($/ton CO2). The basicrojected carbon price is derived from the dynamic integratedodel of climate and the economy (DICE), which was developed

y Nordhaus (2008) at Yale University. DICE is a top-down macro-conomic model, which estimates the cost of carbon required tochieve targeted levels of emission reductions by using a pricef carbon required to increase fuel costs. Nordhaus noted thathe baseline temperature projections by the DICE model are inhe lower-middle end of the projections analyzed in IPCC’s Fourthssessment Report (2007). This report gives the best estimate of

he global mean temperature increase, as between 1.8 and 4.0 ◦C at090–2099 relative to 1980–1999. The DICE model’s baseline yields

global mean temperature increase of 2.2 ◦C over the same periodNordhaus, 2008).

The DICE model’s near-term projections considered variouscenarios for global carbon, including prices for carbon wheretmospheric stabilization occurs at 1.5, 2, and 2.5 times the currentoncentration of CO2; various levels of increased temperatures;yoto Protocol outcomes with and without the participation of thenited States; and a number of carbon control proposals (Nordhaus,008). The policy considered here was the original version of the

rotocol with the United States, where Annex I countries (includinghe United States) collectively agreed to reduce their GHG emis-ions by 5.2% on average for the period 2008–2012. The carbon priceas set at a given level of $15.02 in 2015, increasing by $15.72 in

2035 $715.00 $709.96 $550.33

Source: World Bank (2009), Palm Oil HQ of Pty Ltd (2010), and OECD/FAO (2011).

2025, and then falling to $14.74 in 2035. These payments shouldfurnish the initial REDD+ project cost of $25 per ha and the annualmaintenance fee of $10 per ha (Thoumi, 2009).

Plantation profitability

Potential revenues from palm plantation were calculated bymultiplying yields from palm oil products with the correspondingprices. A palm productive model derived using empirical data fromthe Indonesia Oil Palm Research Institute (Rhett et al., 2009) wasutilized in this study. In this model, crude palm oil (CPO) yieldsaccounted for 21% of fresh fruit bunches (FFB). It was assumedthat palm kernel (PK) yields account for 5% of FFB and that thePK price was approximately 60% of the CPO price. The average FFBproductive lifetime yield was 16.1 tons per ha and 11.4 tons perha for forests and peatland, respectively (Singh, 2008; Fairhurstand McLaughlin, 2009). Plantation setup, annual operations costs,and logging income in forests or peatland were also considered(Sargeant, 2001). Transport costs for palm oil plantation in this areawere measured using the distance by road and amounted to about$19.3 per ha.

The three projected average palm oil prices (Price 1, Price 2,and Price 3) used in this study are $730.48 per ton, $709.96 perton, and $545.33 per ton, respectively (Table 1). Under Price 1,palm oil is priced at $780 per ton from 2015 to 2019 and at $715per ton from 2020 to 2035 (World Bank, 2009). Under Price 2, theprice of palm oil will be maintained at $709.96 per ton from 2015to 2035 (Palm Oil HQ of Pty Ltd, 2010). Price 3 was derived viathe use of a model jointly developed by the Secretariats of theOrganization for Economic Cooperation and Development (OECD)and the FAO, based on the OECD’s Aglink model and extended byFAO’s Cosimo model. Considerable expert judgment and multiplesources of information were applied at various stages of the process,resulting in a single, unified assessment. The procedure involvedtwo main steps: analyzing replies by OECD countries (and somenon-member countries) to an annual questionnaire circulated mid-year, and application of the modeling framework to facilitate aconsistent integration of this information and to derive an initial

set of global market projections (baseline). The output providesa comprehensive dynamic economic and policy-specific represen-tation of countries leading in the production and trade of majortemperate-zone commodities, including rice and vegetable oils.

H. Lu, G. Liu / Land Use Policy 33 (2013) 54– 60 57

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Fig. 1. Palm-REDD profitability

ccording to the forecast, the palm oil price in the world marketill fall to $520.59 per ton in 2015, decrease to $550.33 in 2021,

nd thereafter, remain unchanged until 2035 (OECD/FAO, 2011).

esults

istribution of future land use indicated by profitability gaps

The palm-REDD profitability gap indicates differences in profitetween palm oil plantations and implementing the REDD+ mech-nism. Palm oil profitability consists of income from lands usedor plantation and less costs from land clearing, heavy equipmentnd furniture, fertilizer, and maintenance fees. For REDD+, profit-bility consists of payments for protected carbon stocks throughorest-retaining activities and less direct costs (setup cost, cost toence off the enrolled area, etc.). This profitability gap projected inur simulation is a complex response to several factors, includingields of palm oil, palm oil prices in the world market, carbon creditayments, etc. Due to these complex interactions, the calculatedrofitability differs considerably under different palm oil price andarbon price scenarios, and deserves close attention.

Fig. 1 shows the distributions of profitability gaps in 2025 and035 under Price 1. It can be seen that the deforestation area will

ncrease dramatically from 2025 to 2035. The differences in colorsyellow-colored pixels in Fig. 2(a) changing to red-colored pixelsn Fig. 2(b)) indicate expanding profitability gaps. Meanwhile, themerging light blue pixels (located near the yellow ones) in Fig. 2(a)ndicate potential newly deforested areas.

Fig. 2 shows profitability gaps in the whole area from 2015 to035. At the beginning, they are negative, thus indicating that landsers earn more from REDD+ activities than from palm plantation.his result is not unexpected, given high carbon credit paymentsnd low FFB yields in the initial stage. During the period 2022–2035,owever, profitability gaps increase dramatically. The key factorriving this change is that unlike the past, when carbon creditsayments primarily affected profitability, FFB yields and palm oilrices now exert major impacts. Such high profitability gaps implyhat preserving forests is not a good option for land users in theong term.

ensitivity analysis

Frey et al. (2004) defined sensitivity analysis as the assessmentf the impact of changes in input values on model outputs. It isainly used to determine which input parameter is more impor-

ant or sensitive toward achieving accurate output values (Saltelli

n 2025 and 2035 under Price 1.

et al., 2000). Sensitivity analysis can be expressed using the follow-ing formula:

Y = X × ̌ + ε, (5)

where Y is an n × 1 vector of dependent variables, X is an n × m(m ≤ n) matrix of independent variables, � is an m × 1 vector ofcoefficients, and � is an n × 1 vector of random disturbances. Keymotivations for performing a sensitivity analysis include identi-fication of key sources of variability and uncertainty in order tofacilitate model development, verification, and validation; prior-itization of key sources of variability and uncertainty in orderto prioritize additional data collection and research; and generalmodel refinement (Frey et al., 2004). Marshal (1999) indicated fiveadvantages of using sensitivity analysis: (1) it shows how signifi-cant any given input variable is in determining a project’s worth;(2) it is an excellent technique to help in anticipating and preparingfor the “what if” questions that are asked in presenting and defend-ing a project; (3) it does not require the use of probabilities, as domany techniques for treating uncertainty; (4) it can be used onany measure of project worth; and (5) it requires fewer resources(less information and less time) compared to more sophisticatedtechniques.

Sensitivity analyses were conducted to check the robustnessof the quantitative analytical model presented in this study. Byusing such a method, it is possible to identify the parameters exert-ing greater effect on the modeling results. For a realistic range ofassumptions, the sensitivity analysis can reveal how profitable orunprofitable the REDD+ project might be if input values to the anal-ysis were changed. Thus, if the financial benefits from carbon creditsare not comparable with those of plantation, the proposed REDD+project activity will not be financially attractive. Here, the carbonprice and palm oil price values were chosen as changing variables,to ascertain their influences on the profitability gap.

First, a one-way sensitivity analysis was conducted by changinga single key parameter by an increment or decrement of about 20%of its original value. For each parameter change, the percentageimpact on the model’s outcome/profitability gap was recorded andshown graphically in the form of a tornado diagram (Fig. 3). It can beseen that the change in incremental profitability gaps ratio is onlyabout 18% when the carbon price increases by 20%. In contrast, theratio changes by noteworthy 117% as the price of palm oil increasesby almost the same percentage (from $545.33 to $773.03).

While a one-way sensitivity analysis is useful in demonstratingthe impact of varying one parameter in the model, it may be nec-essary to examine results when two or more parameters changesimultaneously. In this study, this approach involves the changing

58 H. Lu, G. Liu / Land Use Policy 33 (2013) 54– 60

Fig. 2. Palm-REDD profitability gaps under Price 1, Price 2, and Price 3 from 2015 to 2035. (For interpretation of the references to color in the text, the reader is referred tot

otrT

(f$Tp

he web version of the article.)

f two key parameters (carbon price and palm oil price) and showshe results for each potential combination of values within a givenange. The outputs of a two-way sensitivity analysis are shown inable 2.

We computed the profitability gaps for 11 levels of carbon price0%, 10%, 20%, 30%, 40%, 50%, −10%, −20%, −30%, −40% and −50%)

or 11 levels of palm oil prices ($545.33, $592.16, $621.78, $653,685.91, $709.96, $720.59, $730.48, $746.86, $757.15, and $773.03).he profitability gap at the palm oil price of $653 and the carbonrice of the original DICE model (100% DICE) was set as the baseline.

This implies that the REDD+ project will become more viable com-pared to the palm oil plantation due to a low palm oil price anda high carbon price. As expected, however, the profitability gapsincrease as the price of palm oil increases. Different scenarios ofpalm oil prices reveal a noticeable effect of plantation demandson land use. At a constant carbon price (100% DICE), moving from

$545.33 to $773.03 shows a noticeable 155% increase in the pro-fitability gap. In particular, a decrease of only 44% could be foundat the lowest palm oil price ($545.33) and the highest carbon price(+50% DICE).

H. Lu, G. Liu / Land Use Policy 33 (2013) 54– 60 59

Table 2Two-way sensitivity analysis.

Palm oil price

545.33 592.16 621.78 653 685.91 709.96 720.59 730.48 746.86 757.15 773.03

Carbon price +0% Profitability Gap(%) 61.1 73.7 86.6 100.0 113.9 174.8 128.3 194.1 199.4 143.2 216.9+10% Profitability Gap(%) 52.4 65.0 77.9 91.3 105.2 166.1 119.6 185.4 190.7 134.5 208.2+20% Profitability Gap(%) 43.7 56.3 69.2 82.6 96.5 157.4 110.9 176.7 182.0 125.8 199.5+30% Profitability Gap(%) 34.9 47.6 60.5 73.9 87.8 148.7 102.2 168.0 173.3 117.1 190.8+40% Profitability Gap(%) 26.2 38.9 51.8 65.2 79.1 140.0 93.4 159.3 164.6 108.4 182.1+50% Profitability Gap(%) 17.5 30.2 43.1 56.5 70.4 131.2 84.7 150.6 155.9 99.7 173.4−10% Profitability Gap(%) 69.8 82.4 95.3 108.7 122.6 183.5 137.0 202.8 208.1 151.9 225.6−20% Profitability Gap(%) 78.5 91.1 104.0 117.4 131.3 192.2 145.7 211.5 216.9 160.6 234.3−30% Profitability Gap(%) 87.2 99.8 112.7 126−40% Profitability Gap(%) 95.9 108.5 121.4 134−50% Profitability Gap(%) 104.6 117.2 130.1 143

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onclusions

Although analyzing all the components of the REDD+ mech-nism is technically complex, using a spatially explicit model,his study provides a useful illustration of some of the issueselated to compensating land users for protecting existing forests.ore detailed models are necessary to appreciate fully the manyanifestations of the REDD+ mechanism. On the other hand, the

mplications of avoided deforestation are much broader than sim-le opportunity cost calculations: these implications usually goeyond financials and are complicated by political economy issues.urthermore, the negative effect of decline in agricultural land can-ot be simply compensated through cash payments to land usersTacconi, 2007; Hunt and Yungaburra, 2008).

However, this framework of analysis allowed a synthetic androductive visualization of the REDD+ strategy aimed at addressinghe issue of opportunity costs from palm oil plantations in centralalimantan, Indonesia. Our model simulation and sensitivity anal-sis explored how the palm oil prices affect the implementationf REDD+. The results so far show that profitability gaps increaseignificantly as the palm oil price in the world market increases.n the contrary, the effect of carbon prices, as they currently stand,

emains limited. Further analysis indicated remarkable changes inhe profitability gap depending on the price of palm oil; a change inalm oil price from $545.33 to $773.03 shows a large 155% increase

n the profitability gap. Unfortunately, the most likely forecasts ofalm oil prices continue to predict large differences in the profit-bility gap, favoring palm oil plantation over REDD+ projects.

Moreover, since country participation is voluntary, it is difficultor governments to ensure that a REDD+ program paying land userso reduce emissions by protecting forests “reaches the ground.” Theontinuing high demand for biofuels and food puts carbon stocks

.1 140.0 200.9 154.4 220.2 225.6 169.3 243.1

.8 148.7 209.6 163.1 228.9 234.3 178.0 251.8

.5 157.4 218.3 171.8 237.6 243.0 186.7 260.5

in tropical forests at risk, and in doing so, potentially underminesefforts to stabilize the atmospheric CO2 concentration throughREDD+.

Our study focused on the central Kalimantan region, whereforests are directly threatened by future palm oil plantations. How-ever, this research also provides a possible means to evaluatewhether the financial benefits from carbon compensation fromREDD+ projects could be financially attractive in other forest areasof the world.

Acknowledgements

The authors thank the anonymous reviewers whose commentsand suggestions were very helpful in improving the quality ofthis paper. This project is funded with support from NationalBasic Research Program of China (973 Program) 2012CB955800(2012CB955804), 10JJDZONGHE015, NSFC41171438, EBLU2010-01NSY-Suneetha, NSFC 41201602, 2012M521390, SBGJ090110 andHD-ZHS-1203. The first author thanks the support for his researchwork by Japan Society for the Promotion of Science (JSPS) andUnited Nations University-Institute of Advanced Studies. He is alsoindebted to Dr. Suneetha Mazhenchery Subramanian and Prof.Hiroji Isozaki for their assistance.

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