Abatement and Transaction Costs of Carbon-Sink Projects Involving Smallholders

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1 Abaemen and Transacion Coss of Carbon-Sink Projecs Involving Smallholders Oscar Cacho and Leslie Lipper NOTA DI LAVORO MARCH 2007 CCMP Climae Change Modelling and Policy Oscar Cacho, School of Economics, Universiy of New England, Ausralia Leslie Lipper, Agriculural and Developmen Economics Division, Food and Agriculure Organizaion, Rome, Ialy This paper can be downloaded wihou charge a: The Fondazione Eni Enrico Maei Noe di Lavoro Series Index: hp:// Social Science Research Nework Elecronic Paper Collecion: hp://ssrn.com/absrac= The opinions expressed in his paper do no necessarily reflec he posiion of Fondazione Eni Enrico Maei Corso Magena, 63, Milano (I), web sie:

2 Abaemen and Transacion Coss of Carbon-Sink Projecs Involving Smallholders Summary Agroforesry projecs have he poenial o help miigae global warming by acing as sinks for greenhouse gasses. However, paricipaion in carbon-sink projecs may be consrained by high coss. This problem may be paricularly severe for projecs involving smallholders in developing counries. Of paricular concern are he ransacion coss incurred in developing projecs, measuring, cerifying and selling he carbon-sequesraion services generaed by such projecs. This paper addresses hese issues by analysing he implicaions of ransacion and abaemen coss in carbonsequesraion projecs. A model of projec paricipaion is developed, which accouns for he condiions under which boh buyers and sellers would be willing o engage in a carbon ransacion ha involves a long-erm commimen. The model is used o idenify criical projec-design variables (minimum projec size, farm price of carbon, minimum area of paricipaing farms). A projec feasibiliy fronier (PFF) is derived, which shows he minimum projec size ha is feasible for any given marke price of carbon. The PFF is used o analyse how he ransacion coss imposed by he Clean Developmen Mechanism of he Kyoo Proocol affec projec feasibiliy. Keywords: Agroforesry, Climae Policy, Carbon Sequesraion Coss JEL Classificaion: Q23, Q57, O1, O13 This research was funded by he Ausralian Cenre for Inernaional Agriculural Research (ACIAR) and by he Food and Agriculure Organizaion of he Unied Naions (FAO). This paper was presened a he Workshop on Climae Miigaion Measures in he Agro-Foresry Secor and Biodiversiy Fuures, Triese, Ocober 2006 and joinly organised by The Ecological and Environmenal Economics - EEE Programme, The Abdus Salam Inernaional Cenre for Theoreical Physics - ICTP, UNESCO Man and he Biosphere Programme - MAB, and The Inernaional Insiue for Applied Sysems Analysis - IIASA. Address for correspondence: Oscar Cacho Universiy of New England School of Economics Armidale NSW 2351 Ausralia ocacho@une.edu.au

3 1. Inroducion Concerns over global warming have led o he esablishmen of markes for greenhouse gas emissions. The mos common greenhouse gas, and he main gas emied by burning fossil fuels, is carbon dioxide (CO 2 ). Carbon rading has grown significanly since he Kyoo Proocol was raified, reaching a value of US$10 billion in 2005 (Capoor and Ambrosi, 2006). Mos ransacions have occurred wihin he European Union Emission Trading Scheme. However, he focus of his sudy is Aricle 12 of he Kyoo Proocol, he Clean Developmen Mechanism (CDM), which has he purpose of assising developing counries o achieve susainable developmen while conribuing o mee he emission-reducion commimens agreed upon by Annex I counries 1. The medium of exchange under his Aricle is he CER (Cerified Emission Reducion), measured in onnes of CO 2 equivalens (CO 2 e). The demand for CERs will be me mosly by he energy secor, hrough clean echnologies. However, ree-based sysems also have a role o play, as hey are a convenien way of reducing ne emissions by sequesering CO 2 from he amosphere hrough he process of phoosynhesis. Under he curren rules of he CDM afforesaion and reforesaion (AR) are he only allowable means of sequesering carbon. CERS are awarded for AR aciviies ha generae sequesraion addiional o a baseline (or business as usual) esimae. AR projecs in ropical counries may involve paricipaion of smallholders and communiies or hey may be based on indusrial planaions. Smallholder projecs consis of aciviies underaken by farmers who manage small land areas and whose producion sysem may be a mix of subsisence and markeable crops. Indusrial planaions generally consis of monoculure of commercial rees for imber, pulp or frui producion. There is much ineres in he developmen and environmenal communiies in he poenial for small-scale carbon projecs o simulaneously conribue o rural developmen and climae change miigaion. However high ransacions coss associaed wih smallholder paricipaion is a major barrier o be overcome. In his paper we presen an economic model of he decision o paricipae in carbon sequesraion projecs from boh a buyer and seller perspecive, including he impac of ransacions coss. In he following secion we lay ou basic economic issues in carbon sequesraion supply and ransacions coss, followed by a model of projec paricipaion for buyers and sellers. Secion 4 includes a discussion of how key model parameers are consruced, including sequesraion raes and paymens, abaemen coss and ransacions coss. In secion 5 he simulaion model is presened wih he resuls shown in secion 6. Secion 7 presens he concep of he projec feasibiliy fronier and sensiiviy analysis. The paper concludes wih a discussion of means o faciliae he feasibiliy of smallholder carbon projecs based on model resuls, and a discussion of fuure possible exensions of he model. 2. Carbon Sequesraion Supply and Transacions Coss Carbon sequesraion projecs differ in erms of cos per uni of carbon emissions avoided or carbon sequesered, deermined by he opporuniy coss of swiching land uses.they also differ in erms of oher environmenal and social benefis provided. For example, a complex 1 Annex I counries include he OECD counries (excep Mexico and Turkey) and ransiion economies in easern Europe. The US and Ausralia did no raify he Proocol and he bulk of demand for carbon credis comes from Europe and Japan. 1

4 agrofores may represen an efficien use of family labour, provide susenance and conain higher biodiversiy han a monoculure of a fas-growing ree species. A large-scale monoculure planaion, on he oher hand, may accumulae more carbon and provide employmen, bu i may provide lile biodiversiy and social benefis besides employmen. These issues need o be considered by hos counries when designing policies o encourage he adopion of carbon-sequesraion projecs ha also provide environmenal and social benefis. The supply of CERs depends on availabiliy and coss of differen echnologies and resource endowmens, and hese will be parly deermined by locaion. In Figure 1 he poenial supply funcion in he absence of ransacion coss (S A ) represens he marginal abaemen coss of providing differen cumulaive levels of emission reducions. For a given supply funcion, as deermined by curren echnology and land availabiliy, he equilibrium levels of price and quaniy (Q A, P A ) depend on he demand funcion (D). The curve S A shows he prices ha would be required o moivae differen levels of abaemen, or miigaion, in a world of zero ransacion coss, where supply decisions depend simply on abaemen coss. P ($) S T C T S A P T P A D Q T Q A Q (CERs) Figure 1. The marke for CERs and he role of ransacion coss In order o receive cerificaion and ener he CER marke, a projec will have o incur various ransacion coss in showing ha i is reducing ne emissions (e.g. increasing ne sequesraion). Carbon sequesered and sored in agroforesry projecs needs o be accouned for in a way ha ensures he carbon changes are real, direcly aribuable o he projec, and addiional o any changes ha would have occurred in he absence of he projec. Transacion coss (C T ) make he supply funcion shif up and o he lef (from S A o S T in Figure 1), hence reducing he size of he marke. The new equilibrium poin (Q T, P T ) represens a lower quaniy of CERs a a higher price compared o he original equilibrium (Q A, P A ). If he ransacion coss are oo high, he marke will no develop a all. This sudy focuses on he supply side of he marke and concenraes on agroforesry projecs involving smallholders. 3. A Model of Projec Paricipaion Including Transacions Coss Consider a projec composed of one buyer and many sellers. The Buyer is an NGO (he projec proponen) and he Sellers are smallholders. The Sellers are paid for adoping 2

5 agroforesry land uses ha sequeser carbon above a baseline. The Buyer purchases hese carbon offses and sells hem in he CER marke. So he Buyer acs as an inermediary beween he smallholders and he inernaional carbon marke. For simpliciy, define a represenaive farmer wih a given farm area a and curren land use, call his he average seller and assume here are n idenical sellers. The represenaive seller will paricipae in he projec if he reward received for carbon sequesraion (v C ) is larger han he opporuniy cos of swiching land uses (he abaemen cos, v A ) plus he ransacion cos of paricipaing in he projec (v T ), The condiion for seller paricipaion is: v > v + v (1) C A T wih he hree variables measured in erms of presen value. The presen value of carbon paymens received by he seller is: v C F ( + ) = a C p 1 δ (2) s where C represens he expeced sock of carbon above he baseline per hecare of land in year, p F is he farm price of carbon and δ S is he Seller s discoun rae. The abaemen cos o he Seller is: v A R ( + ) = a 1 δ (3) s Where R represens he opporuniy cos experienced in year as a resul of having swiched land use o a ree-based sysem in year zero. The ransacion cos experienced by he seller is he discouned sum of a sream of annual ransacion coss (q ): ( + ) v T = q 1 δ s (4) Now consider he Buyer. The Buyer will implemen a projec if he presen value of carbon paymens received in he CER marke (V C ) is a leas equal o he presen value of paymens o smallholders (he abaemen cos o he buyer, V A ) plus he ransacion coss of designing and implemening he projec (V T ). The condiion for Buyer paricipaion is: V V + V (5) C A T V C is he discouned sum of paymens obained by accumulaing he carbon offses produced by all landholders in he projec, cerifying hem and selling hem in he CER marke: V C = n a p C C ( + δ ) B 1 (6) where p C is he renal price per onne of carbon and δ B is he Buyer s discoun rae. The abaemen and ransacion coss for he Buyer are, respecively: 3

6 V A = n a p F C ( + δ ) B 1 (7) ( + ) V T = Q 1 δ B (8) where Q represen he annual ransacion coss. The Buyer mus se he farm price of carbon (p F ) a a level ha saisfies condiions (1) and (5). This decision is influenced by he size of he projec and he number of paricipans, as explained laer. 4. Consrucing key parameers in he model A. Projecing carbon sequesraion raes and paymens The carbon emission reducion available for credis in a given year (C ) is only he amoun of carbon sequesered under he projec ha is above he baselinetha is, only he addiional emissions reducions relaive o he business-as-usual scenario are eligible. In any given year: C CP, CC, = (9) Where C P, and C C, are he expeced carbon socks in he proposed land use and he curren land use, respecively, in year. If ime series daa on diameer and heigh of rees are available for he sie, he amoun of carbon sequesered by aboveground biomass can be esimaed based on allomeric equaions (Brown, 2002). Alernaively, projecions of carbon socks can be based on models (i.e. Wise and Cacho 2005a, 2005b). Regarding carbon paymens, o avoid he problem of permanence 2 Marland e al. (2001) propose he use of a renal price. The difference beween he purchase and he renal sysem is ha he former represens a purchase of carbon flows wih redempion of paymens upon projec erminaion or failure (Cacho, Hean and Wise 2003), whereas he laer involves a renal of carbon socks wih no redempion of credis required. Boh sysems are compaible wih emporary CERs for AR projecs under he CDM 3, bu he renal sysem is more convenien for modelling purposes. The range of farm prices (p F ) ha he buyer can pay is influenced by he marke price of carbon (p C ). Here we express p F and p C as annual renal prices per uni of biomass carbon sored in rees. To undersand he relaionship beween renal prices and purchase prices consider he presen value (PV) of an asse ha yields a perpeual sream of annual paymens Y discouned a rae i: PV Y = 1 e i (10) 2 The permanence problem arises in afforesaion and reforesaion projecs because carbon capured in rees can be released upon harves, in conras wih energy projecs where an avoided emission is permanen. 3 A emporary CER or CER is a CER issued for an AR projec aciviy which expires a he end of he commimen period following he one during which i was issued (UNFCCC documen FCCC/CP/2003/6/Add.2). 4

7 In a perfec marke he raio Y/PV is equivalen o he renal price of he asse expressed as a proporion of he asse s value. If we le he asse be a CER (expressed as a onne of CO 2 ) valued a price p CER, and consider ha he process of phoosynhesis convers 3.67 unis of CO 2 ino one uni of biomass carbon, hen he renal price of biomass carbon is: i ( 1 ) CER p C = 3.67 e p (11) The value of he discoun rae in he renal carbon marke (i) depends on he rae of reurn expeced by invesors. For simpliciy we assume he carbon marke discoun rae is he same as he Buyer s. Therefore he value of i in (10) and (11) is calculaed by convering he rae for discree discouning δ B ino a coninuous rae i = ln(1+δ B ). The CER price places an upper limi on he feasible farm price, because he Buyer would se p F p C even in he absence of ransacion coss. The relaionship beween he purchase price and he renal price is affeced no only by he discoun rae bu also by expeced price rends. If he price of carbon is expeced o increase in he fuure hen he renal price will be lower han indicaed by equaion (11), because hose rening will require a discoun o forego he opion of purchasing oday. Conversely, if he price of carbon is expeced o decrease in he fuure he renal price will be higher han indicaed by equaion (11). B. Abaemen Coss Abaemen coss for he Seller are defined as he coss of producing one uni of (uncerified) carbon sequesraion services, or he cos of producing one uni of biomass carbon. In any given locaion, abaemen coss can be esimaed as he opporuniy cos of underaking a carbon-sequesraion aciviy raher han he mos profiable alernaive aciviy, or he cos of swiching from he previous land use o he new land use, as represened in equaion (3). This cos includes he presen value of he sream of revenues foregone as a resul of paricipaing in he projec. I may also include addiional risk exposure or loss of food securiy arising from his paricipaion (Cacho, Marshall and Milne 2003). If we ignore risk percepions and oher barriers o adopion ha could be overcome by paricipaing in he projec, he opporuniy cos from equaion (3) is: R RC, RP, = (12) where R C, and R P, are he annual ne revenues of he curren land use and he proposed land use respecively. In agroforesry sysems wih muliple oupus (eg. frui, imber and spices) he annual revenue is he sum of he revenues obained from he differen producs. In a sysem wih J land uses and I inpus we have: R P, = y j, p j xi, ci, j (1,...,J), i (1,...,i) (13) j i Where, y j, is he yield of oupu j in year, p j is he price per uni of oupu, x j, is he amoun of inpu i used in year and c i is he cos of inpu i. 5

8 C. Transacion Coss Williamson (1985) disinguished he coss of conracing as ex ane and ex pos ransacion coss. These correspond wih aciviies underaken in he processes of achieving an agreemen and hen coninuing o coordinae implemenaion of he agreemen, respecively. Savins (1995, p. 134) saed: ransacion coss are ubiquious in marke economies and can arise from he ransfer of any propery righ because paries o an exchange mus find one anoher, communicae, and exchange informaion. In he case of carbon markes ransacion coss end o be high, because he propery righ o be exchanged is difficul o measure and is exac size is subjec o uncerainy. Cacho, Marshall and Milne (2003, 2005) presen a ypology of ransacion coss applicable o carbon-sink projecs, largely based on Dudek and Wiener (1996). Here we aggregae heir seven caegories ino five and disinguish beween he coss borne by buyers and sellers (Table 1). 6

9 Table 1. Classificaion of ransacion coss in AR projecs for carbon sequesraion Cos ype Buyer (Q) Seller (q) Search and negoiaion W S find sies, esablish conac, organize informaion sessions, draf conracs, provide raining, promoion esablish baseline for region esimae poenial C socks and flows of projec design individual farm plans produce PDD ex ane w S aend informaion sessions underake raining design farm plan Approval W A approval by hos counry (DNA) validae he projec proposal (DOE) Submi o CER Board ex ane obain permi w A Projec managemen W P buy compuers and sofware, esablish office esablish permanen sampling plos ex ane w P purchase ape and equipmen for measuring rees and sampling soil ex pos mainain daabase and adminiser aend regular projec meeings paymens coordinae field crews, pay salaries disribue paymens o landholders ineres coss Monioring W M ener daa from farmer shees calculae C paymens process soil C samples measure random sample of plos o check farmer esimaes verificaion and cerificaion of carbon (DOE) ex pos w M measure rees, fill in form and deliver o projec office sample soil C Enforcemen and insurance W E mainain buffer of C purchase liabiliy insurance sele dispues ex pos w E proec plo from poachers and fire paricipae in dispue selemen 7

10 The ransacion coss experienced by buyers and sellers in ime period are respecively: Q WS, + W A, + W P, + WM, + WE, = (14) q = ws, + wa, + wp, + wm, + we, (15) where he subscrips represen search and negoiaion (S), approval (A), projec managemen (P), monioring (M), and enforcemen and insurance (E). Using he CDM projec cycle as a basis (Figure 2) we can relae hese coss o he design and implemenaion of projecs. (1) PDD Developmen ex ane (pre-implemenaion) (2) approval by hos counry (3) validaion (4) regisraion (5) monioring ex pos (implemenaion) (6) verificaion + cerificaion (7) CER issuance Figure 2. The CDM projec cycle Search and negoiaion coss. The CDM projec cycle sars wih he preparaion of a Projec Design Documen (PDD). This requires he projec developer o idenify a suiable region; gaher agriculural, social and economic informaion abou he region o develop he baseline; idenify suiable land uses and esimae heir carbon sequesraion poenial; conac and esablish relaionships wih he local people; negoiae he erms of he projec and he schedule of paymens for carbon-sequesraion services; and possibly underake environmenal and social impac sudies. These aciviies are included wihin Search and negoiaion coss in Table 1. Esimaes of hese coss in he lieraure vary widely depending on he naure of he aciviies wihin he projec, he scale of he projec, assumpions regarding he presence of local NGOs and farmer groups ha may faciliae he process of conacing local people, and he availabiliy of local expers o design he monioring sraegy and prepare he PDD. Approval coss. Seps 2, 3 and 4 of he CDM cycle in Figure 2 fall wihin he Approval coss caegory. They include approval by he Designaed Naional Auhoriy (DNA) of he hos counry; validaion of he PDD by a Designaed Operaional Eniy (DOE) accredied by he CDM Execuive Board; and regisraion of he projec when submied o he Execuive Board. The coss of hese aciviies depend on several facors, including he insiuional infrasrucure of he hos counry and he availabiliy of a local DOE ha can validae he PDD as a cheaper alernaive o an inernaional consulan. 8

11 Monioring coss. Seps 5, 6 and 7 of he CDM cycle in Figure 2 fall wihin he Monioring coss caegory of Table 1. These are he coss of measuring he CO 2 abaemen acually achieved by he projec, including cerificaion and verificaion by a DOE. Once he CDM Execuive Board issues he appropriae number of CERs he projec developer (he Buyer) becomes a seller in he inernaional carbon marke. Any addiional ransacion coss ha may be associaed wih selling CERs in he inernaional marke are no accouned for below. I is assumed ha he projec developer can access he full price per CER, alhough i is a simple maer o reduce he price by a brokerage fee if applicable. Monioring coss are recurren, as hey are incurred every ime a new bach of carbon is submied for CER crediing. Two ypes of ransacion coss lised in Table 1 do no fi nealy wihin he CDM projec cycle; noneheless hey are necessary for he approval and operaion of he projec. Projec managemen coss include he cos of keeping records of projec paricipans and adminisraion of paymens o sellers, as well as salaries and ransporaion coss of projec employees. Ex ane projec managemen aciviies include he esablishmen of a local projec office and he raining of saff. Projec managemen coss are no normally recognized explicily in he lieraure on ransacion coss of Kyoo mechanisms, bu hey are expenses incurred in buying and selling carbon-sequesraion services, so hey should be considered. Enforcemen and insurance coss arise from he risk of projec failure or underperformance, which migh be caused by fire, slow ree growh, or leakage 4. Enforcemen coss may be incurred in he form of liigaion and dispue-resoluion expenses. Insurance opions may include purchase of an insurance policy, deducion of a risk premium from he price of carbon, and mainenance of buffer carbon socks ha are no sold. These aciviies form par of he risk-managemen sraegy required wihin he PDD. D. Empirical Esimaes of Transacion Coss A review of published CDM ransacion-cos esimaes for small projecs (Michelowa e al 2003; de Gouvello and Coo 2003; Krey 2004; EcoSecuriies 2003) indicaes ha search and negoiaion coss (W S ) range beween $22,000 and $160,000; approval coss (W A ) range beween $12,000 and $120,000; and monioring coss (W M ) range beween $5,000 and $270,000. Only one source (EcoSecuriies) presens risk-miigaion coss (1% o 3% of CERs), which fall under enforcemen and insurance (W E ). The wide range of values in all caegories illusraes he fac ha ransacion coss are highly sensiive o he ype and size of projec assumed. In addiion, since he marke is very recenly esablished, here is sill considerable variaion and discussion abou he rules of exchange which affec ransacions coss. Useful informaion regarding ransacion coss of projecs involving smallholders is provided by he Scolel Te projec in Souhern Mexico, which has developed a managemen sysem called Plan Vivo. De Jong e al. (2004) ouline he ransacion coss associaed wih designing he Plan Vivo Managemen Sysem. Under he Search and negoiaion caegory we could include he coss of underaking he feasibiliy sudy, he carbon invenories, he landuse analysis, and he developmen of he regional baseline. The oal cos of hese aciviies was approximaely $830,000. Trained echnicians develop Plan Vivos in heir communiy 4 Leakage occurs when he emissions reducions achieved in he projec area are offse by an increase in emissions ouside he projec boundary, leading o no ne reducion in emissions (or even a ne increase). 9

12 eiher wih individual farmers or wih he communiy as a whole. Designing a Plan Vivo requires abou 3 days of raining by a professional echnician. Salary, ranspor and lodging, are he main expendiures for raining sessions, which ypically cos beween $400 and $500 each (de Jong e al. 2004). Arifin (2005) presens esimaes of he ransacion coss incurred by communiy-based foresry managemen groups in Sumber Jaya, Indonesia. Aciviies idenified by Arifin include obaining informaion and joining farmer groups (search and negoiaion); he cos of obaining a permi o paricipae (approval); he cos of aending meeings (projec managemen); and he coss of guarding crops and paricipaing in dispue selemen (enforcemen and insurance). Arifin calculaed hese coss as he ime required o perform hese aciviies muliplied by he wage rae. 5. Implemenaion of a Numerical Model for Empirical Analysis In his secion we exend our model o enable us o underake empirical analysis of siuaions in which boh buyers and sellers condiions for paricipaing in he carbon marke are me, including he effecs of ransacions coss as laid ou above. The model, which is implemened in he Malab environmen (The Mahworks 2000), can be solved numerically for any se of exogenous variables. Essenially, he model consiss of a se of nonlinear equaions ha are solved ieraively o find combinaions of variables ha saisfy projec-paricipaion consrains. In Table 2 a lis of variables and heir unis as defined for he model is presened, covering various dimensions of abaemen and ransacions coss, reurns o carbon sequesraion and oher key facors ha influence projec feasibiliy. The foregoing analysis, based on a hypoheical 25-year projec, is used o idenify criical projec-design variables. Prices are expressed in erms of US dollars. The baseline is assumed o be a cassava crop wih an NPV of $4,376/ha and he projec aciviy is a damar agroforesry sysem wih an NPV of $4,372/ha. The damar sysem is a complex agrofores developed by he Krui people of Lampung, souh Sumara. The sysem consiss of a sequence of crops building up o a climax ha mimics maure naural fores (ASB 2001). The main ree species is damar (Shorea javanica), a source of resin ha provides a flow of income. Oher oupus include fruis, pepper and firewood. The carbon sock of he baseline was assumed o be zero because cassava biomass is harvesed every year and soil carbon is no accouned for. The carbon accumulaion paern of he damar sysem (Figure 3) was represened by a Gomperz equaion: C P exp ( γ ) α, = β (16) β 10

13 Table 2. Variable definiions for projec-paricipaion model Variable Descripion Unis V C, v C Carbon paymens received by Buyer, Seller $ (presen value) V A, v A Abaemen coss experienced by Buyer, Seller $ (presen value) V T, v A Transacion coss experienced by Buyer, Seller $ (presen value) C Carbon sock above he baseline in year C/ha C P, Carbon sock of projec aciviy in year C/ha C C, Carbon sock of curren aciviy (baseline) in year C/ha R Opporuniy cos of land use change in year $/ha R P, Ne revenue of projec aciviy in year $/ha R C, Ne revenue of baseline in year $/ha A Average farm area Ha p F Farm price of carbon $/Tc p C Renal price of carbon $/C p CER Purchase price of CER $/CO 2 e P L Price of labour $/pd N Number of paricipaing farms Farms δ B Buyer discoun rae (%) δ S Seller discoun rae (%) y j, Yield of produc j in year unis/ha a p j Price of produc j $/uni a x i, quaniy of inpu i in year unis/ha b c j cos of inpu i $/uni b Q Toal Buyer s ransacion coss in year $ q Toal Seller s ransacion coss in year $ a oupu unis vary (eg kg,, m 3 ) depending on he ype of produc b inpu unis vary (eg pd, kg, bag) depending on he ype of inpu wih parameer values α=0.5, β=471.6 and γ= These parameer values resul in an average carbon sock of 89.3 C/ha over he 25-year period of he projec. This agroforesry sysem will coninue o capure carbon afer he projec ends (Figure 3). 500 Biomass carbon (C/ha) Year Figure 3. Simulaed biomass carbon rajecory for damar in Sumara; he hypoheical projec duraion is indicaed by a doed line 11

14 Transacion cos assumpions are presened in Table 3. Noe ha he unis of measuremen of hese coss vary. In he case of he Buyer, coss can be ex-ane fixed coss ($), annual fixed coss ($/y), or variable coss dependen on he number of paricipaing farms ($/farm) or on he size of he projec ($/ha/y). In he case of he Seller, coss are expressed in erms of labour. The original five ransacion-cos caegories are disaggregaed o accoun for variaion in he unis of measuremen. The expanded classificaion is presened under Cos ype (column 1, Table 3), where number subscrips denoe he differen cos ypes. For example, here are hree ypes of monioring coss; W M1 ($/ha/y), W M2 ($/y), and W M3 (CER/y). Table 3. Transacion cos assumpions in base case Cos ype Aciviy Cos Unis Buyer (projec manager) W S1 consulaion and negoiaion 20,000 $ W S1 esablish baseline and C flows of projec for region 20,000 $ W S1 design monioring plan 5,000 $ W S1 prepare projec design documen 6,500 $ W S2 design individual farm plans 200 $/farm W A approval by hos governmen 1,000 $ W A validae he projec proposal (DOE) a 6,000 $ W A submi o CER Board (Regisraion fee) * $ W P1 purchase IT infrasrucure, esablish local office 20,000 $ W P2 mainain daabase/sofware and adminiser paymens 10,000 $/y W P2 coordinae field crews, pay salaries 40,000 $/y W M1 measure C socks in sample of farmers plos 8 $/ha/y W M2 verificaion and cerificaion of carbon by DOE 10,000 $/y W M3 adapaion fee 0.02 CERs/y W E1 mainain buffer of C 0.10 CERs/y W E2 sele dispues 100 $/farm/y Sellers (farmers) w S aend informaion sessions 6 d w S underake raining 10 d w S design farm plan 4 d w A obain permission o paricipae in projec 4 d w P aend regular projec meeings 5 d/y w M measure rees and repor resuls o projec office 3 d/ha/y w E proec plo from poachers and fire 10 d/y w E paricipae in dispue resoluion 2 d/y * Regisraion fees vary wih projec size <15,000 CERs=$5,000; 15,000 o <50,000 CERs=$10,000; 50,000 o <100,000 CERs=$15,000; 100,000 o < 200,000=$20,000; >200,000 CERs = $30,000 a Designaed Operaional Eniy Monioring coss of AR projecs can be high, and designing he righ monioring sraegy is imporan (Cacho, Wise and MacDicken 2004). Monioring also involves verificaion and cerificaion of carbon socks by a designaed operaional eniy (DOE). This is assumed o cos $10,000 per year (Table 3), bu he cos could be higher if inernaional expers are required or he projec sies are scaered over a large area. 12

15 Designing individual farm plans (W S2 ) involves a echnician visiing each farm and drawing a land-use change plan in consulaion wih he farmer. This is assumed o cos $200 per farm o he Buyer, which would include one or wo days of a local echnician s ime plus ravel expenses. This aciviy would also ake four days of he Seller s ime. Enforcemen and insurance is assumed o involve mainaining a buffer of 10% of biomass carbon no sold as CERs, plus an average cos of $100 per farm per year o sele dispues; his expense would include any legal fees involved. The buffer is also a risk-miigaion sraegy o accoun for leakage or he possible loss of rees. Using he expanded noaion inroduced in Table 3, ransacion coss can now be calculaed as: V T = W S1 + W W A M 3 + W + W P1 E1 + nw ( + aw ) j S 2 + n W + P2 M 2 E 2 ( W + W )( C C ) 0 p C M 1 + ( 1+ δ ) B (17) v T = ws + wa + [ wp + we + a wm ]( + δ S ) pl 1 (18) Assumpions regarding prices and discoun raes are presened in Table 4. The price of CERs is se iniially a a high value ($20/ CO 2 ) o ensure he projec is feasible. Table 4. Oher assumpions for base case Variable Value Descripion p CER 20 price of CERs ($/ CO2e) p C 4.28 farm price of carbon ($/ C) p L 1.72 price of labour ($/d) n 500 number of farms in projec a 2 average area of farm (ha) δ B 0.06 Buyer discoun rae δ S 0.15 Seller discoun rae i ln(1+δ B ) discoun rae in carbon renal marke 89.3 mean carbon sock (C/ha) for Damar 0 mean carbon sock (C/ha) for Cassava (baseline) 4,372 ne presen value ($/ha) of Damar 4,375 ne presen value ($/ha) of Cassava (baseline) Replacing equaions (4) and (8) wih (17) and (18) respecively, and insering parameer values in he appropriae equaions, we can now solve he model and deermine under wha condiions boh buyers and sellers will paricipae in he marke; based on condiions for projec paricipaion (1) and (5). Experimens consis of solving he model for differen values of p CER, p F, a and n and deermining when boh condiions (1) and (5) are saisfied. 13

16 A series of compuer experimens were performed on he hypoheical projec. The model is buil upon he assumpion ha he projec consiss of n idenical farms each consising of a hecares. The projec developer esablishes individual conracs whereby farmers agree o change heir land use from cropping o agroforesry and receive paymens for he carbon capured in heir rees. In designing he projec he Buyer decides on he number of paricipans (n), he carbon price paid o farmers (p F ) and oher feaures such as monioring and risk-miigaion sraegies. 6. Model Resuls A. Deermining he Feasible Range for Farm prices The firs sep in he numerical analysis is o deermine bounds for he farm price. This involves finding he minimum price accepable o he Seller (p S ) and he maximum price he Buyer is willing o pay (p B ). Firs, p F is se such ha v C v A =v T and he resuling value is called p S ; hen p F is se such ha V C V A =V T and he resuling value is called p B. The projec is feasible only if p B p S, and he farm price falls wihin he range p S p F p B. The acual value of p F depends on he marke power of he paricipans, he objecives of he Buyer and he oucome of negoiaions. $ (A) Seller v C -v A v 400 T (B) Buyer $ V T 1000 p S p B V C -V A Farm price ($/C) Figure 4. The feasible range of farm prices wihin which he projec will be feasible is derived by finding he minimum price accepable o he Seller in (A) and he maximum price accepable o he Buyer in (B) The carbon margin for he Seller (v C -v A in Figure 4A) increases linearly wih p F, whereas he carbon margin for he Buyer (V C -V A in Figure 4B) decreases linearly wih p F. The inersecions of he carbon margin curves wih heir respecive ransacion cos curves indicae he price bounds (p S, p B ). Given he assumpions in Tables 3 and 4 he feasible farm price ranges beween $0.83/C and $1.31/C. For simpliciy we now se p F = (p S + p B )/2 as 14

17 he base price o deermine he effecs of oher projec design variables; herefore p F = $1.07/C in he base case. B. Deermining Minimum farm size In he base case we assume and average farm area of wo hecares, his size is consisen wih he average area of land graned o ransmigrans in Sumara (Gris and Menz, 1997). The assumpions in Table 4 imply ha he projec covers 1,000 ha (500 farms of 2 ha each) and increases he biomass carbon sock by 89,300 C above he baseline. This corresponds o a oal of 327,731 CERs produced by he projec (89,300 C 3.67 CO 2 /C). Given ha we are dealing wih smallholders i is imporan o deermine o wha exen he size of paricipaing farms affecs he feasibiliy of he projec. To answer his quesion we solve he model for a range of values of a, while simulaneously adjusing n o keep projec size consan a 1,000 ha (or 327,731 CERs). This operaion does no affec he carbon margin bu i has a significan effec on ransacion coss for he Buyer (Figure 5). As farm size increases he Buyer s ransacion coss decrease a a decreasing rae and become relaively fla a farm sizes beyond 5 ha or so. Reducing farm size below 1 ha causes ransacion coss o increase exponenially. The minimum farm size for he given parameers is 1.6 ha, which would require 625 paricipaing farms o mainain oal projec area a 1,000 ha. A his poin he Buyer s ransacion coss would be approximaely $2.42 million, which ranslaes ino $7.39/CER. By comparison, for a projec wih 5-ha farms (requiring 200 farms o mainain he projec area a 1,000ha), he Buyer s ransacion coss would be $1.75M, or $5.34/CER $ V C -V A 1000 V T Farm size (ha) Figure 5. Minimum feasible farm size is indicaed by he doed line a he inersecion of he carbon margin (V C -V A ) and he ransacion coss (V T ) for he Buyer (noe: he number of farms decreases as farm size increases o keep he projec size consan a 1000 ha, farm price is $1.07/C) C. Deermining Minimum number of farms Now assume ha farm size remains consan a 2 ha and he oal projec area can increase by increasing he number of conracs wih farmers. In his case, as he oal projec area increases he farm price he Buyer is prepared o pay (p B ) also increases (Figure 6). This is because, alhough boh he carbon margin (V C -V A ) and ransacion coss (V T ) increase wih he increasing number of paricipans, he laer increases slower because he fixed cos are spread 15

18 among a larger number of paricipans. The Buyer s price increases a a decreasing rae, from $0.81 o $1.91/C as he number of farms under conrac increases from 355 o 1,000; and oal projec area increases from 700 ha o 2,000 ha. In Figure 6, he minimum number of farms (355) is ha a which he Buyer s maximum farm price is he same as he minimum price accepable o he Seller (p B = p S ). Farm price ($/C) infeasible Buyer (p B ) Seller (p S ) Number of farms Figure 6. The breakeven number of farms, indicaed by he doed line, is calculaed as he poin a which he maximum price he Buyer is willing o pay (p B ) equals he minimum price he Seller is willing o accep (p S ) D. Effecs of CER price The CER price used above ($20/CO 2 e) is raher high given curren marke condiions, so i is imporan o deermine how a lower price will affec projec feasibiliy. In paricular, i is of ineres o evaluae how he CER price affecs he criical values of p S, p B, n and a idenified above. Essenially, his involves changing p CER and repeaing he above analysis o idenify he poins a which he Buyer s carbon margin (V C -V A ) equals he ransacion cos (V T ). Resuls are presened in Table 5. The middle column of resuls shows he base case already discussed, he oher wo columns are he resuls wih p CER values of $25 and $15. Given he ransacion coss assumed and he defaul number of farms (500) and farm size (2 ha), a p CER of $15 is no feasible. A his CER price he Buyer s price (p B =0.39) is below he Seller price (p S =0.83). Seing he farm price p F a is lowes feasible value of $0.82/C, we find ha he minimum farm area wih consan projec size (1,000 ha) is 3.43 ha. This resul (Block A in Table 5) is represened by downward shif of he V C -V A line in Figure 5 as he CER price decreases, causing he new inersecion wih V T o occur a a larger farm size. The las hree rows of Table 5 (he Block labelled B) are he mos ineresing, because hey show he absolue minimum possible projec size (when p F = p S ), or he breakeven projec size, raher han he minimum projec size wih p F arbirarily se a he mean beween Buyer s and Seller s prices. The breakeven number of farms increases from 355 a a p CER of $20 o 772 a a p CER of $15. This shif represens a doubling in projec area from 710 ha o 1,544 ha and is equivalen o an increase in projec size (in erms of CERs) from 233 k CO 2 e o 506 k CO 2 e. 16

19 Table 5. Effec of CER price on criical values of projec-design variables Price of CERs ($/CO 2 e) Seller minimum carbon price ($/C), p S Buyer maximum farm price ($/C), p B Farm price ($/C), p F A) Wih projec area consan (1000ha): Minimum farm area (ha) Corresponding number of farms Projec CERs (CO 2 e) 327, , ,891 B) Wih farm size consan (2ha) and p F =p S : Breakeven number of farms Corresponding projec area (ha) ,544 Projec CERs (CO 2 e) 150, , ,250 To pu our resuls in perspecive consider ha, in May 2006, here were 176 CDM projecs regisered 5, claiming o reduce emissions by an average of 301,633 CO 2 e/y. Classified by size, here were 71 large-scale projecs wih average emission reducions of 638,133 CO 2 e/y and 78 small-scale projecs claiming an average of 29,554 CO 2 e/y. To conver our resuls from socks of carbon o flows of CO 2 and compare hem o exising projecs, noe ha he aboveground biomass carbon sock of he damar sysem is assumed o increase from 0 o 252 C/ha in 25 years (Figure 3); his represens an annual CO 2 reducion of 37 onnes ( /25); muliplying his value by he breakeven projec areas in Table 5 we obain 17,020 CO 2 /y, 26,233 CO 2 /y and 57,128 CO 2 /y for CER prices of $25, $20 and $15 respecively. So our hypoheical projec may fi wihin he small-scale caegory a a CER price of $20 or above. 7. The Projec Feasibiliy Fronier and Sensiiviy Analysis We have seen above ha smaller projecs become feasible as he CER price increases. Ofen, i is convenien o express projec size in erms of oal CERs raher han number of farms, as his allows comparison wih oher projecs, including hose in he energy secor. Figure 7 shows how he minimum projec size (in erms of CERs) decreases as he CER price increases. This curve forms a fronier, because projecs falling below or o he lef of his curve are no feasible under he given ransacion coss, whereas projecs ha fall above or o he righ of he fronier are feasible. We will call his curve he projec feasibiliy fronier (PFF). 5 hp://cdm.unfccc.in/projecs/regisered.hml 17

20 300 Projec CERs (k CO 2 e) PFF feasible area CER price ($/ CO 2 e) Figure 7. The projec feasibiliy fronier (PFF) In essence he PFF is he se of poins a which he carbon margins jus cover he ransacion coss for boh paries. The breakeven value of n is hen convered o CER unis wih he formula: Projec CERs = n a (ha) 89.3 (C/ha) 3.67 (CO 2 /C). The PFF is a convenien way of exploring he influence of land produciviy, individual ransacion coss, or any oher exogenous variable on he viabiliy of a projec. A new PFF can be derived by changing any exogenous variable and repeaing he process; hus providing a useful ool for sensiiviy analysis. A. Effec of carbon sequesraion poenial The damar sysem in our projec is assumed o increase average carbon sock by 89.3 onnes per hecare over he life of he projec (25 years). Bu here can be considerable variabiliy in he produciviy of farms wihin he same region. Therefore i is imporan o deermine he influence of carbon-sequesraion poenial on projec viabiliy. Figure 8 presens PFFs for hree levels of carbon sequesraion poenial: he base case, a low poenial (0.75 C()), and a high poenial (1.25 C()). A change in carbon sequesraion poenial causes he PFF o shif in he opposie direcion. When C() increases by 25% he PFF shifs lef, so ha, compared o he base case, smaller projecs are viable a a given CER price; or lower CER prices are required o make a given projec size viable. A decrease in C() has he opposie effec, and he effec is more pronounced. These resuls indicae ha a reducion in acual carbon sequesered relaive o expecaions can have a major influence on he success of he projec. 18

21 400 Projec CERs (k CO 2 e) (1.25C ) (0.75C ) CER price ($/ CO 2 e) Figure 8. The effec of carbon sequesraion poenial on he posiion of he projec feasibiliy fronier; he doed line represens he base case, solid lines represen an increase (o 1.25 base) or a decrease (o 0.75 base) in he carbon-sock rajecory B. Effec of Transacion coss The ransacion coss assumed for his analysis were presened in Table 3. These values are arbirary bu plausible. There is high uncerainy regarding some of hese coss and hus i is imporan o evaluae heir effec on projec viabiliy. This can be done by modifying he Seller s ransacion coss, q(), and/or he Buyer s ransacion coss, Q(), and solving he model. Figure 9 presens PFFs for hree ransacion-cos scenarios: he base case, low Buyer cos (0.75 Q()), and a low Seller cos (0.75 q()). 400 Projec CERs (k CO 2 e) (0.75Q ) (0.75q ) CER price ($/ CO 2 e) Figure 9. The effec of ransacion coss on he posiion of he projec feasibiliy fronier; he doed line represens he base case, he solid lines represen a 25% decrease in he ransacion coss of he Buyer (Q ) or Seller (q ) Decreases in ransacion coss cause he PFF o shif lef, making smaller projecs viable a a given CER price; or lowering he CER price required o make a given projec size viable. Buyer s ransacion coss have a more pronounced influence han Seller s ransacion coss. 19

22 So reducing he ransacion coss experienced by buyers should be a prioriy when designing projecs, which is he subjec we urn o in he following secion. 8. Discussion: poenial for improving he feasibiliy of smallholder carbon projecs and fuure research A. Poenial for Reducing Transacions Coss The model resuls indicae ha reducing ransacions coss is an imporan means o increase he feasibiliy of smallholder paricipaion in carbon markes. In his secion we ouline some sraegies by which hese coss can be reduced, and he role of he public secor in faciliaing hem. The bigges gains in improving he feasibiliy of smallholder carbon sequesraion projecs may be realized by reducing he ex-ane ransacions coss he buyers face. Reducing fixed coss may be expeced o have greaer benefis for smallholder paricipaion, since hese are shared over he oal number of hecares in he projec. Sraegies o reduce hese ypes of ransacions coss fall ino hree broad caegories: 1) Increasing projec size by fosering/building upon collecive acion amongs suppliers; 2) reducing conracing coss by uilizing exising managemen srucures; and 3) reducing informaion coss hrough public provision of daa, emplaes and guidelines. The caegories are no muually exclusive and in fac in many cases are complemenary. Foser collecive acion. Coordinaing and consolidaing sequesraion supply among groups of poor landholders is an imporan way o reduce ransacion coss associaed wih smallholder projecs and one which has received considerable aenion in he lieraure. (Lipper and Cavaassi 2004; Cacho e. al. 2003; Smih and Scherr 2002; Landell-Mills and Porras 2002) Examples of projecs involving smallholder coordinaion in he supply of carbon services are described in Cacho e. 2003, Smih and Scherr 2002 and Orlando e. al In hese projecs he coss o buyers of idenifying, conracing, and enforcing viable carbon sequesraion opporuniies among smallholders are reduced hrough he presence of an inermediary represening he suppliers, which can be an NGO, communiy group or governmen agency. I is imporan o noe however, ha he ransacions cos facing he sellers can increase by paricipaing in such group schemes, and his cos mus be lower han he benefis ha sellers derive from paricipaion. Several of he exising carbon smallholder projecs were buil upon some ype of exising communiy projecs, such as ongoing communiy-based naural resource managemen projecs, paricularly communiy foresry projecs or farmer s groups. For example, he Scolel Te projec was iniiaed wih a sakeholder group of ineresed farmers drawn mainly from one farmers union operaing in he Chiapas region. ( hp:// Communiies ha already have experience in working cooperaively are likely o have lower coss of paricipaion as well as dispue resoluion anoher imporan ransacion cos. One poenially imporan area for collecive acion o reduce ransacions coss is hrough peer-monioring schemes. There is anecdoal evidence ha, when farmers learn he value of carbon biomass, hey could monior heir plos a low cos. For example, farmers in Sumara are able o assess he volume of wood in heir rees by sigh; hey are accurae wihin he 0.25 m 3 incremens used in he imber marke (Hairia e al. 2001). In field ess underaken by Delaney and Rosheko (1999), wo days were required for a crew o learn invenory mehods 20

23 for measuring carbon in agroforesry gardens in Java. This evidence suggess ha raining smallholders o idenify and measure heir own rees or paricipae in a peer monioring scheme may be a good invesmen, since monioring coss are a fairly significan recurring ransacion cos. In addiion self or peer monioring sysems have he poenial o yield more accurae carbon assessmens, because he accuracy of carbon measuremens depends on he number of sampling sies (e.g. see Cacho e al., 2004). Therefore involving smallholders in self-monioring could no only reduce monioring and enforcemen coss, bu also achieve high measuremen accuracy by allowing high sampling inensiy a a fairly low cos. Uilize exising infrasrucure/managemen capaciy. Transacions coss associaed wih esablishing local offices, purchasing IT infrasrucure, mainain daabase/sofware and adminiser paymens could be grealy reduced where carbon projecs are implemened by exising public or privae eniies ha already have some or all of he infrasrucure and managemen capaciy in place. Anoher poenially imporan managemen srucure o build upon is condiional cash ransfer mechanisms ha various counries and local governmens are involved in implemening. (See de la Briere and Rawlings 2006 for a summary) These programs have been implemened in a wide range of middle and low income developing counries and hey involve linking cash paymens o behaviour modificaion usually in he area of educaion and healh. Imporan managemen lessons as well as he poenial o use exising paymen infrasrucure can be obained from hese programs and make a significan reducion in fixed ransacions coss facing small-scale sequesraion projecs. Reducing informaion coss. Generaing and disseminaing informaion is one of he larges source of ransacions coss in carbon projecs: including he esablishmen of baselines, mehodologies for implemenaion and monioring, as well informaion on buyers and sellers o reduce search coss. A his poin he carbon marke is sill quie young, so informaion coss are high as a se of rules, mehodologies and baseline daases are currenly under developmen. For example, he esablishmen of carbon baselines is one of he mos expensive ex ane ransacions coss, and he generaion and disseminaion of informaion by developmen agencies can have a considerable effec in reducing ransacion coss. The Good Pracice Guides of he IPCC and associaed ools have reduced he cos of developing projec documens, in paricular hose for small scale projecs, which allow generic parameers o be used o esimae carbon socks of projec aciviies. Anoher imporan aspec of reducing informaion coss is he developmen of a se of rules and mehodologies specific o small scale projecs. Under he CDM small-scale projecs are allowed o adap a simplified se of procedures including a simplified PDD. Haies (2004) saes: The simplified mehodologies adoped by he Execuive Board for small-scale CDM projecs appear o reduce he ransacion coss for hose projecs enough o make such projecs economically viable. Evidence as o wheher he ransacion cos per CER is higher or lower han for a regular CDM projec is mixed. Bu indicaions of a supply of poenial small-scale CDM projecs sugges ha he ransacion coss for he simplified mehodologies are sufficienly low o make some small projecs economically viable a he curren marke price for Kyoo unis. This saemen refers o projecs in he energy secor which end o be easier o monior. I is no clear wheher he same applies o AR projecs. To es wheher his is rue he model can be solved using values represening he simplified modaliies and procedures for small-scale CDM projecs. Therefore i is imporan o obain cos esimaes for such projecs for fuure analyses. Several effors are underway o reduce buyer and seller search and negoiaion coss including he esablishmen of websies such as he Ecosysem markeplace 21