Private valuation of carbon sequestration in forest plantations

Ecological Economics 69 (2010) 451–458

Contents lists available at ScienceDirect

Ecological Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e c o n

Survey

Private valuation of carbon sequestration in forest plantations A. Bussoni Guitart a,⁎, L.C. Estraviz Rodriguez b a b

Facultad de Agronomía, Universidad de la República. Avda. E. Garzón, 780, CP 12.900, Montevideo, Uruguay Escola Superior de Agricultura “Luiz de Queiroz”, Universidad de Sao, Paulo, Brazil

a r t i c l e

i n f o

Article history: Received 19 February 2009 Received in revised form 13 October 2009 Accepted 13 October 2009 Available online 10 November 2009 Keywords: Eucalyptus Cost LEV Carbon Supply

a b s t r a c t Approval of the Clean Development Mechanism, provided for in the Kyoto Protocol, enables countries with afforested land to trade in carbon emissions reduction certificates related to carbon dioxide equivalent quantities (CO2-e) stored within a certain forest area. Potential CO2-e above base line sequestration was determined for two forest sites on commercial eucalyptus plantations in northern Brazil (Bahia). Compensation values for silvicultural regimes involving rotation lengths greater than economically optimal were computed using the Faustmann formula. Mean values obtained were US$8.16 (MgCO2-e)− 1 and US $7.19 (MgCO2-e)− 1 for average and high site indexes, respectively. Results show that carbon supply is more cost-efficient in highly productive sites. Annuities of US$18.8 Mg C− 1 and US$35.1 Mg C− 1 and yearly payments of US$4.4 m− 3 and US$8.2 m− 3 due for each marginal cubic meter produced were computed for high and average sites, respectively. The estimated value of the tonne of carbon defines minimum values to be paid to forest owners, in order to induce a change in silvicultural management regimes. A reduction of carbon supply could be expected as a result of an increase in wood prices, although it would not respond in a regular manner. For both sites, price elasticity of supply was found to be inelastic and increased as rotation length moved further away from economically optimal: 0.24 and 0.27 for age 11 years in average- and highproductivity sites, respectively. This would be due to biomass production potential as a limiting factor; beyond a certain threshold value, an increase in price does not sustain a proportional change in carbon storage supply. The environmental service valuation model proposed might be adequate for assessing potential supply in plantation forestry, from a private landowner perspective, with an economic opportunity cost. The model is not applicable to low commercial value forest plantations. © 2009 Elsevier B.V. All rights reserved.

1. Introduction There is a growing social awareness of potential environmental problems caused by global warming. This is associated with the increase in Greenhouse Effect Gases (GEG), which were first emitted during the Industrial Revolution in the nineteenth century. Even in the most optimistic of scenarios, climate change can be detrimental to several production chains, with a strong impact on developing economies which depend largely on agriculture. Article 12 of the Kyoto Protocol encourages Clean Development Mechanisms (CDM) as an alternative to GEG and enables governments or private entities in developed countries to grant certified emissions reductions (CERs) to emission-reducing projects in developing countries. During the Ninth Conference of the Parties held in Milan (COP9, 2003), a new certificate modality was adopted. CERs may be issued as temporary certified emission reductions (tCER), which expire at the end of each 5-year commitment period, and long-term certified emission reductions (lCER), expiring at the end of the project crediting ⁎ Corresponding author. Fax: +598 2 3542052. E-mail addresses: [email protected] (A. Bussoni), [email protected] (L.C.E. Rodriguez). 0921-8009/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2009.10.005

period, which should be less than 30 years. In the former, the buyer owns the carbon-sequestering biomass and may opt for renewal of certificates at the end of the commitment period. If market alternatives are favorable, he may harvest the timber and sell it, subtracting the emitted units. This mechanism will allow inclusion of fast-growing species in environmental projects with great flexibility, because when market conditions are favorable, stands may be clearcut and timber sold at the end of the period. Methodological details involving accounting and credit duration so far remain unresolved. Accounting options allow generation of tCER on the basis of full growing stock at the moment of verification or during periods that have already occurred (Dutschke and Schlamadinger, 2003). The underlying economic solution is the application of Pigou's concept (Romero, 1994), according to which a central institution establishes socially optimum pollution levels and states quantities to be mitigated, with the object of attaining Pareto efficient externality levels. At this point, no agent could change position and obtain benefits without adverse effects on other agent; from any initial resource assignment, that would be the best solution for the agents involved. The size of the carbon sink market during the first commitment period (2008–2012) ascribable to forestry is estimated at 350 to 680 M tonne CO2 (Uruguay, 2002). These quantities are expected to rise as countries

452

A. Bussoni, L.C.E. Rodriguez / Ecological Economics 69 (2010) 451–458

agree on higher reduction levels in their economies and endeavor to transfer obligations to other economic agents. Land Use, Land Use Change and Forestry (LULUCF), mentioned in Article 3 of the Kyoto Protocol, is limited to afforestation or reforestation activities during the first commitment period; this implies human-induced change or conversion to forestry through plantation, direct seeding or natural seeding. Changes in silvicultural management can alter carbon stocking and enable stored carbon valuation. In the case of plantations, increases in stored carbon are brought about by regimes that enhance long-lived wood product volume. In the case of existing forests, carbon sink enhancing is related to lower deforestation rates. However, in recent negotiations concerning CDM, cases where additional carbon sequestration is induced by changes in silvicultural regimes of existing forests have not been considered eligible. Such changes could be environmentally and economically equal to or even more convenient than other mitigation policies. In the case of market goods production in a state of concurrence, the price of such products would reflect the opportunity cost of the production factors involved. Transactions have been recorded in the carbon market, where published values on environmental projects were US$1 Mg C− 1 stored in forests in Bolivia and up to US$22 Mg C− 1 in Brazilian forests (World Bank, 2003). Average carbon prices negotiated varied from US$2 Mg C− 1 on cash market contracts at the Chicago Climate Exchange (CCE, 2006), to future market contracts at the European Climate Exchange with price quotes between US$20 and US$35 Mg C− 1 (ECE, 2006). Price variability brings into question the worth of stored carbon for the forest owner and the amount to be paid, either to be included in the production function or as a carbon sequestration environmental objective. Simulation of forest plantation-stored carbon has been based on potential subsidies. Through this mechanism, Nordhaus (1991) simulated the production of durable goods, computing global potential carbon storage. Gong and Kriström (1998) estimated optimum rotation in a forest yielding wood and carbon through a subsidies program. The objective equation to be maximized comprises both net revenues from timber harvest and subsidies. Díaz Balteiro and Romero (2001) computed the price of carbon as an equivalent subsidy, which equates the private optimum or maximum Net Present Value (NPV) to the environmental optimum (maximum biomass production). Richards and Stokes (2004) review research papers on carbon sequestration costs published during the last 12 years; they point out that disregarding the length of the period during which carbon sequestration actually takes place hinders adequate comparison of technological alternatives. Huang and Kronrad (2001) simulated the level of monetary compensation to be paid to non-forest or forest landowners to sequester carbon, as the difference between the maximum Land Expectation Value (LEV) and the age of maximum carbon Mean Annual Increment (MAI). Benítez and Obersteiner (2005) computed minimum carbon price considering agricultural rent as land capital benefit. Creedy and Wurzbacher (2001) examine strategies for a forest yielding water, wood and carbon, applying Faustmann's multiple rotation model. Age structure is among the main factors influencing carbon storage capacity. The value of a tonne of carbon, estimated at between US$3.93 and US $250 Mg C− 1, is computed from the estimate of shadow-price of storage carbon using alternative technologies. However, the landowner's opportunity cost is not taken into account. Plantinga et al. (1999) considered agricultural rent equalled opportunity cost, and carbon sequestration cost estimates ranged from US$75 to 120 Mg C− 1. This was regarded as favorable when compared with alternative production technology costs, adopted in order to reduce carbon emissions, which were estimated between US$100 and 900 Mg C− 1. Nevertheless, Fearnside (1995) points out the difficulties of computing agricultural rent in Brazil, which adds to the problems of determining opportunity cost of land on the basis of market value. Romero (1996) regarded carbon sequestration as a community benefit that results in a divergence between private and social optima

arising between the forest owner, who produces wood, and social demand for keeping the forest uncut. Optimal rotation age estimates are computed using compromise programming. Van Kooten et al. (1995), established the production possibility frontier (PPF) between NPV at harvest time and CO2 storage. Hoen and Solberg (1994) measured the marginal cost of CO2 stocking as the NPV reduction from timber that must be accepted in order to increase the amount of carbon captured. Knoke and Weber (2006) estimated the carbon price as the distance from optimal value, which is computed with social and environmental restrictions considered ecological and social functions. Lewis et al. (1996) computed carbon sequestration cost as land opportunity cost or land rent plus silvicultural costs of stock and administration. This procedure is suitable for a regional scale, but its shortcoming is that it does not identify the optimum regime; neither are management alternatives compared in terms of relative efficiency. Forest carbon sequestration cost studies have been undertaken for a great diversity of forest types, species and sites, including forest plantations and the management of existing forests (Richards and Stokes, 2004). Other researchers have also been concerned with valuation of sequestered carbon tonne issues (Huang and Kronrad, 2001). However, few of these studies have dealt with measuring carbon sink potential and carbon sequestration costs of fast-growing species such as the eucalyptus, cropped in short (6 to 12-year) rotations for short-lived wood products such as pulpwood and fuel, a widespread silvicultural system in most of the Southern Hemisphere. The purpose of this work is to suggest a method for computing the minimum value of a tonne of stored carbon payable to the owner of the forest, in order to justify the adoption of a silvicultural regime that would increase carbon sequestration. The resulting method should be applicable to plantation forestry in companies where carbon sequestration has been adopted as an environmental management objective. This objective will become common practice in companies seeking forest management certification under local standard UNIT (2006) 1152:2006; criterion 5 of this standard concerns the upkeep of forest contribution to global carbon cycle. 2. Methods In this study a deterministic approach was adopted and the following assumptions were made. Eucalyptus plantation forestry is a private profitable investment with no government compensation, and the main object of the landowner is to maximize land commercial benefit. The only commercial product obtained is pulpwood; amenity consumption or multiple use were not considered. At the end of each rotation, stands are clearcut and replanted (i.e., coppice is not involved in stand replacement). Discount rate is the same for both pulpwood production and carbon sequestration. No climatic or biological risks of damage affect either wood production or carbon sequestration. Environmental losses due to plantation forestry with exotic species do not change when the rotation period is extended. Data used here were taken from the original database of Stape (2002), a PhD thesis carried out in the state of Bahia, Brazil, which dealt with the effects of environmental factors on the growth of Eucalyptus grandis × urophylla clonal stock, cropped on 14 sites in short rotations, ranging from 6 to 8 years (Fig. 1). The fourteen sites included in the original study were grouped in two classes. When ranked according to mean annual increment and aboveground net primary production, nine of the sites were highly productive, with average dominant height at age 5 years ranging from 20.5 to 29.2 m. The remaining five sites had average productivity, with average dominant height at 5 years between 17.4 and 20.5 m. Stape (2002) determined total fresh and dry weight from stem, coarse root, branch and foliage biomass on trees sampled in each site at ages ranging from 27 to 103 months. Dominant soils are ultisols, oxisols and quartzipsaments; these are acidic (pH 3.5–5.6 in water), with low to moderate organic C (2–6 kgCm−2,

A. Bussoni, L.C.E. Rodriguez / Ecological Economics 69 (2010) 451–458

453

Table 1 Gompertz equation parameters for wood and bark volume. Average-and highproductivity sites.

a Lower limit/upper limit b Lower limit/upper limit k Lower limit/upper limit R2 adjusted Estimate standard error Transformation

Fig. 1. Location and characteristics of the 14 stands⁎. ⁎ Stape J. (2002).

−2

from 0 to 0.6 m) and N (100–500 g N m ). Rainfall decreases from 1600 mm yr− 1 in coastal areas to less than 800 mm yr− 1 just 120 km inland (Stape, 2002). Foliage values were excluded in the present study because dead leaf biomass counterbalances live foliage biomass in carbon sequestration. These data were used to fit equations estimating stem wood, bark, branch and primary root biomass. A valuation methodology including the alternative economic activity of these forests, that is, wood production, was used. A basic premise is the existence of a tonne of carbon value (pCO2), which adequately compensates plantation managers for reduced income resulting from the adoption of silvicultural regimes that beneficially increase average carbon storage. Therefore, a p⁎ value of indifference must exist, as a break-even point between continuance of working with current silvicultural regimes or else change to alternative systems which increase carbon storage rates. The break-even value p⁎, is the amount which equalizes the willingness to adopt regimes involving only wood production with the willingness to adopt regimes which also include carbon sequestration. This p⁎ value could be used as a minimum reference price, in order to estimate the value of carbon stored in the forest growing stock. The Gompertz (1832) model was used to estimate wood and bark volumes, using the Marquardt non-linear regression procedure of SAS (Statistical Analysis System). Branch biomass estimates were obtained using the PROC NLIN SAS procedure for the model relating tree biomass and age, which was adapted from the one used by Karmacharya and Singh (1992) for teak. In order to estimate primary root biomass, PROC REG regression equations cited in literature for hardwoods were applied in SAS (Li et al., 2002; Tables 1–3). 2.1. Baseline Estimation Procedure The original situation was modeled without including carbon sequestration, computing total carbon and carbon values stored in different portions of the tree. Lengthening stand rotation age would enhance carbon sequestration and thus enable the issue of CERs. Carbon supply was estimated and the price to be paid for the inclusion of an environmental objective to silvicultural management was simulated. Valued captured carbon is the incremental amount of sequestered carbon in relation to previous state of affairs, considered the baseline. Resulting mean values for high and average productivity sites are 71.84 and

Average productivity sites

High-productivity sites

Bark

Wood

Bark

Wood

1.422172242 1.22/1.62 6.9016 1.64/12.16 0.0526126 0.030/0.07 0.79 0.18 ln

3.48459 3.27/3.69 2.14878 0.83/3.46 0.0511411 0.029/0.072 0.79 0.23 ln

2.074177 1.45/2.69 3.81998 0.95/6.68 0.03711675 0.011/0.063 0.80 0.21 Ln

4.303969 3.73/4.87 1.7696150 0.399/3.139 0.487972 0.140/0.835 0.76 0.30 ln

Model parameters. Gompertz (1832) model used for estimating wood and bark volume. −b e− k t W = e Ae , where W = size of the organism at time t (Mg ha− 1), A = asymptotic value that the organism may attain, k = a relative measure of the organism's growth rate, which expresses the curve slope, b = time selection, t = time in years. t = year. All parameter values are different than/from zero (P ≤ 0.05).

42.40 Mg C ha− 1 (Tables 4 and 5, respectively). Extending rotation age to 11 years would increase these values to 98.88 and 50.40 Mg C ha− 1, for high- and average productivity sites, respectively. Figs. 2 and 3 show the average change in carbon sequestration when lengthened rotation is maintained for a 60-year period for average- and high-productivity sites, respectively. Carbon storage fees are computed as compensation for not harvesting all carbon sequestered above such baseline values. Forestry activities in which carbon is stored independently from the environmental project do not produce CER rights. That concept differentiates forests planted originally with the purpose of wood production, which also effectively capture carbon but do not generate emission trading, from other forests planned considering carbon sequestration. The baseline of a given forest site is the amount of carbon captured in commercial plantation conditions, tonne of carbon stored at maximum economic return rotation age for the forest species. Tree carbon amounts were computed and carbon retained at maximum LEV was estimated as baseline (Faustmann, 1849; Samuelson, 1976). Marginal carbon sequestration curves were obtained as the difference between carbon stored at maximum LEV age and carbon stored at a given age. Marginal variations in soil carbon storage were not considered. Paul et al. (2002) and Stape (2002) did not find significant differences in soil carbon storage under several silvicultural regimes for Eucalyptus stands. Carbon concentration in total biomass was assumed to be 45% (Stape, 2002) and wood basic density used was 0.50 g cm− 3 (Carvalho and Camargo, 1996; Vital et al., 1985). GEG international commercial unit is the equivalent tonne of carbon dioxide, MgCO2-e,1 which is adopted in the present work. Prices of wood paid to landowners were, from regional estimates, between US$5.5 and US$9.5 m− 3. Costs of carbon capture verification and monitoring of US$4 ha− 1 were computed, based on previous projects (Brown et al., 2000). Because carbon sequestered throughout the life cycle of products is not recognized as CDM, its instant release to the atmosphere after the harvest is assumed. Annual average discount rate (11%) represents mean long-term interest rate in Brazilian economy. This value is made up of required return and risk premium. The former was estimated as 5.75% with reference to 6-month London Interbank Offered Rate (Libor rate), averaged for the last 12 years (Commercial Property Finance Brokers, 2002). The latter, risk premium, was 5% (Instituto Pesquisas Econômicas—IPEA, 2003). International institutions, such as the World Bank, for similar investment projects, also use this rate. 1

A tonne of sequestered carbon equals 3.67 MgCO2-e.

454

A. Bussoni, L.C.E. Rodriguez / Ecological Economics 69 (2010) 451–458

Table 2 Branch volume parameters for average-and high-productivity sites.

a b R2 adjusted Model

Table 4 Carbon in biomass compartments at -productivity sites.

Average productivity sites

High-productivity sites

− 0.967706 0.512495 0.64 ln W = a + b · log t

0.746784 0.207694 0.37

2.2. p⁎ value Determination The value of the carbon sequestration environmental service is quantified as the private opportunity cost of keeping the growing stock biomass uncut. The original premise is that silvicultural management regimes in which the main objective is carbon storage alter the nature of forest products. Therefore, forest biomass production management has two simultaneous and conflicting goals: wood yield and carbon storing. Rotation age that maximizes wood volume will then be different from rotation age maximizing carbon sequestration. The amount of carbon reverted to the atmosphere is a function of rotation age. The forest owner undertakes the cost of keeping uncut growing stock, that is, the opportunity cost of timber growth and soil occupation. Mixed production regimes will yield income equal to the sum of wood plus the value of pCO2 sequestered tonne, multiplied by the amount of carbon stored: T′

T′−t

Rfc;CO2 = pco2 ∑ ΔCO2t ð1 + rÞ t =0

T′

T′−t

+ ∑ Pfc Vt ð1 + rÞ

ð1Þ

t =0

Age (years)

Bark (Mg C ha− 1)

Branch (Mg C ha− 1)

Stem (Mg C ha− 1)

Coarse Roots (Mg C ha− 1)

Total carbon content (Mg C ha− 1)

2 3 4 5 6 7 8 9 10 11

1.54 2.14 2.97 3.95 4.92 5.76 6.43 6.93 7.27 7.51

4.08 4.44 4.72 4.94 5.13 5.30 5.45 5.58 5.70 5.82

9.11 17.43 28.43 39.99 50.15 58.03 63.64 67.42 69.87 71.44

5.50 7.09 8.82 10.39 11.64 12.56 13.20 13.63 13.92 14.12

20.23 31.10 44.94 59.26 71.84 81.65 88.72 93.56 96.78 98.88

Where m′t includes establishment, silvicultural treatment, maintenance and harvest costs during year t in a mixed production process compounded to the end of the period. Additionally, KCO2, the maintenance cost of carbon stock captured is: T′

′

T′−t

KCO2 = ∑ vt ð1 + rÞ

ð5Þ

t =0

where v′t includes sequestered carbon verification and monitoring costs. In a forest where only wood is produced, LEV will be: LEVfc =

Rfc −Kfc

ð6Þ

″

½ð1 + rÞT −1

Income obtained exclusively with wood production is, T″

T″

T ″−t

Rfc = ∑ Pfc Vt ð1 + rÞ

ð2Þ

t =0

Where Rfc,CO2 = income compounded to the end of rotation, computed from wood sold and carbon sequestered (US$); pCO2 = value of a tonne of sequestered carbon (US$ Mg C− 1); t stands for any given age; T′= rotation age for wood production and carbon sequestration regime; ΔCO2 t = net incremental amount of carbon stored during period t and t − 1; r =discount rate (%); Pfc =wood price (US$ m− 3); Vt = volume harvested in successive t periods (m3 ha− 1); Rfc = income compounded to the end of rotation, from the sale of wood alone (US$); and T″=rotation age for wood production only regime. Forest LEV is the discounted value of the infinite future net benefits from goods and services produced (Samuelson, 1976). In the case of a forest with mixed production, wood and carbon sequestration: LEVfc;co2 =

Rfc;co2 −Kfc;co2 −KCO2 ½ð1 + rÞ −1

ð7Þ

t =0

Where Kfc is silvicultural cost in a wood-producing forest, m″t includes establishment, silvicultural treatment, maintenance or harvest costs during year t of the wood production process compounded to the end of the period (Table 6). The pCO2 value is assumed to be unknown, but a p⁎ indifference value is defined as that which makes mixed production LEV equal to LEV when only wood is produced. ð8Þ

LEVfc;CO2 = LEVfc T′

T′

∑ p⁎ΔCO2t ð1 + rÞT′−t −KCO2 + ∑ Pfc Vt ð1 + rÞT′−t −Kfc;CO2 t

t =0

′

½ð1 + rÞT −1 T″

∑ Pfc Vt ð1 + rÞT

ð3Þ

T′

T ″ −t

Kfc = ∑ m″t ð1 + rÞ

=

″

−t

t =0

ð9Þ

−Kfc

″

½ð1 + rÞT −1

Where Kfc,CO2 is silvicultural cost in a forest with mixed production. T′

′

T′−t

Kfc;CO2 = ∑ mt ð1 + rÞ

ð4Þ

t =0

Table 3 Coarse root volume parameters for average-and high-productivity sites.

a Lower limit/upper limit b Lower limit/upper limit R2 adjust Estimate standard error Model

Average sites

High sites

0.383566 0.229/0.5377 0.870356 0.758/0.982 0.85 0.93 R = a · Wb

1.136575738 0.344/1.929 0.562397346 0.392/0.732 0.65 2.1

Table 5 Carbon in biomass compartments at average productivity sites. Age (years)

Bark (Mg C ha− 1)

Branch (Mg C ha− 1)

Stem (Mg C ha− 1)

Coarse Roots (Mg C ha− 1)

Total carbon content (Mg C ha− 1)

2 3 4 5 6 7 8 9 10 11

1.22 1.65 2.27 2.89 3.38 3.70 3.90 4.01 4.07 4.11

2.07 2.38 2.72 3.07 3.40 3.71 3.96 4.17 4.33 4.45

6.40 11.92 18.13 23.41 27.14 29.49 30.87 31.65 32.09 32.32

3.15 4.82 6.43 7.67 8.47 8.96 9.23 9.39 9.47 9.51

12.85 20.77 29.55 37.03 42.40 45.86 47.96 49.22 49.96 50.40

A. Bussoni, L.C.E. Rodriguez / Ecological Economics 69 (2010) 451–458

455

Table 6 Current Eucalyptus production costs.

Fig. 2. Carbon supply and average carbon. High-productivity sites.

Reordering,

T′

T″

′

∑ ΔCO2t ð1 + rÞT −t −KCO2

p⁎t = 0

T′

−

=

½ð1 + rÞT ′ −1 T ′ −t

∑ Pfc Vt ð1 + rÞ

t =0

∑ Pfc Vt ð1 + rÞT

″

−t

t =0

−Kfc

½ð1 + rÞT ″ −1

ð10Þ

″

p⁎ =

″

−t

t=0

−Kfc

″

½ð1 + rÞT −1

T′

½ð1 + rÞ −1 T

′

ð11Þ

′

∑ ΔCO2t ð1 + rÞT −t −KCO2

t=0 T′

′

∑ Pfc Vt ð1 + rÞT −t −Kfc;CO2

0 −t= T′

Manual pre-plantation cleaning cut First initial ant control Deep ripping Total manual cleaning Mechanized broadcast fertilization Mechanized total/broadcast herbicide application Plantation + seedling transport Second initial ant control Blanking. seedling and transport Phosphate application/fertilization Manual/inter-row herbicide application/on the planting row Manual post-plantation cleaning cut Patrolling Mechanized herbicide application between planting rows Total Establishment Costs Management costs year 0 Management costs year 1 Management cost

0 0 0 0 0 0

17.8 9.4 33.4 18.8 32.9 22.0

0 0 0 0 0

106.2 5.8 15.6 45.7 14.3

0 0 0

7.1 2.8 11.4

Maintenance additional/complementary Fertilization Firebreak maintenance

The expression to the left of the equation is the value of environmental benefit and the right-hand expression is the private cost of changing silvicultural management, computed as the difference between wood LEV and new forest income due to wood production with a new rotation.

T

Time (year)

Mechanized broadcast herbicide Application Ant control

−Kfc;CO2

½ð1 + rÞT ′ −1

∑ Pfc :Vt ð1 + rÞT

Activity/action

′

∑ ΔCO2t ð1 + rÞT −t −KCO2

t =0

Finally, p⁎ is the price which balances the environmental market and the wood market. In this work, the cost of opportunity of land is included as discounted future forestry land capital benefits in perpetuity.

Pre-harvest ant control Pre-harvest mechanized cleaning Pre-harvest manual cleaning Pre-harvest costs

0 1 From year 2 to rotation age 1.2.3 From year 1 to rotation age 1 From year 1 to rotation age Rotation age Rotation age Rotation age Rotation age

Cost (US$ ha− 1)⁎

343 78.9 10.5 9.8 15.4 6.1 11.7 3.7 14.9 1.5 12.0 28.4

Note: Based on data reported in Stape (2002).⁎ US dollars. November 2002.

3. Results The value of a tonne of carbon was computed for each age and forest site considered. As we move away from the maximum LEV point, the amount to be paid to the forest owner increases. Value zero (p⁎ = 0) for the tonne of carbon is a consequence of no additional carbon stored and corresponds to maximum LEV attained under market conditions described above; this value is reached at age 6 years for both sites. The forest owner does not receive any monetary compensation for the continuance of the same activities, thus retaining no additional carbon in biomass, as is shown on Table 7. Table 7 Value and marginal carbon capture in high and average productivity sites. Age (years)

High-productivity sites

Average productivity sites

p⁎

p⁎

Marginal carbon

Marginal carbon

US$ (Mg CO2-e ha− 1) US$ (Mg CO2-e ha− 1) (MgCO2-e)− 1 (MgCO2-e)− 1

Fig. 3. Carbon supply and average carbon. Average productivity sites.

2 3 4 5 6 7 8 9 10 11 NPV p⁎ annual NPV p⁎

− 2.3 − 2.5 − 2.3 − 1.6 0.0 0.8 2.7 5.2 8.1 11.5 18.9 5.1

− 189.4 − 149.5 − 98.7 − 46.2 0.0 36.0 61.9 79.7 91.5 99.2

− 3.1 − 2.9 − 2.4 − 1.3 0.0 2.9 6.0 9.7 14.1 19.3 35.4 9.6

− 108.5 − 79.4 − 47.2 − 19.7 0.0 12.7 20.4 25.0 27.7 29.4

456

A. Bussoni, L.C.E. Rodriguez / Ecological Economics 69 (2010) 451–458

Fig. 4. Sequestered carbon above the baseline.

As biomass grows, rotation departs from maximum LEV age when compensation value would be higher. Negative values of a sequestered tonne of carbon (p⁎ b 0) express how much the forest owner should pay per sequestered tonne of carbon in compensation for not changing silvicultural regimes. On the other hand, positive values would be the minimum monetary compensation the forest owner should receive for changing silvicultural management. Net sequestered carbon supply curves as a function of p⁎ value computed to compensate forest owners for carbon sequestration are shown in Fig. 4. Positive amounts of carbon captured in forest biomass produce increasing values of captured tonne of carbon and also add to carbon supply. High-productivity sites are more efficient in carbon sequestration, as they capture more carbon at the same p⁎ value. Computations were performed for age 11 years, as shown in Tables 8 and 9. If the wood market improves, optimal rotations would be shortened as a result, which would in turn increase opportunity cost for private agents, that is, the cost of maintaining growing stock biomass uncut, thus prolonging rotation. A reduction of carbon supply could be expected as a result of an increase in wood prices, although it would not respond in a regular manner. In high-productivity sites, carbon supply decreases between US$5.5 m− 3 and US$7.5 m− 3 and then increases essentially back to the US$5.5 m− 3 level after the wood price has risen further to US $9.5 m− 3 (Fig. 5). In contrast, for average productivity sites, an increase in the price of wood from US$5.5 m− 3 to US$9.5 m− 3 raises the carbon supply curve (Fig. 6). The increased carbon supply is due to the increase in maximum LEV age from 5.5 to 6.5 years.

Table 8 −1 Carbon tonne value (US$ MgCO2-e ) high-productivity stands—age 11. Discount rate (%) 9 10 11 12

Price cellulose wood (US$ m− 3) 5.5

6.5

7.5

8.5

9.5

7.17 7.80 8.46 9.13

9.67 10.47 11.29 12.13

12.17 13.13 14.12 12.25

14.67 12.73 13.77 14.84

13.86 15.01 16.21 17.44

Table 9 1 Carbon tonne value (US$ MgCO2− -e ) average productivity stands—age 11. Discount rate (%) 9 10 11 12

Price cellulose wood (US$ m− 3) 5.5 7.74 8.36 9.01 9.69

6.5

7.5

8.5

9.5

12.81 10.65 11.46 12.29

14.31 15.32 16.38 17.47

18.73 19.99 16.74 17.91

18.33 19.64 20.99 22.40

Fig. 5. Carbon supply and wood price. High-productivity site.

Fig. 6. Carbon supply and wood price. Average productivity site.

Variations in the amount of carbon sequestered on average-and high-productivity sites with the variation of discount rate and wood prices are shown in Tables 8 and 9, respectively. For both sites, price elasticity of supply was found to be inelastic and increasing as rotation length moved further away from economically optimal: 0.24 and 0.27 for age 11 years in average-and high-productivity sites, respectively. This would be due to biomass production potential as a limiting factor; beyond a certain threshold value, an increase in price does not sustain a proportional change in carbon storage supply. 4. Discussion Land opportunity cost is often equaled to a real estate market value as in Lewis et al. (1996) and Moulton and Richards (1990). Even when these values reflect economic assets potential to some degree, they may be influenced by other variables, such as speculation, and do not adequately represent productive potential (Fearnside, 1995). However, this would be unlikely in a scenario of competitive market without land use change and with constant prices, as the one assumed in this study. The cost of carbon sequestration is estimated as a flux of forest products, which ceased to be harvested. Average values for additional carbon sequestration were US$8.16 MgCO2-eyr− 1 and US$7.19 MgCO2-eyr− 1, on average- and highproductivity sites, respectively; for the sake of clarity, prices are denominated by CO2. These values are expressions of indifference thresholds toward changes in rotation length when selecting silvicultural management regimes producing more biomass and capturing additional carbon. An annuity of US$18.8 Mg C− 1− and US$35.1 Mg C− 1 for high and average sites, respectively, can be computed from values in Table 7. If a tonne of carbon equals 4.27 m3 of wood, respective yearly payments due for each marginal cubic meter produced are US$4.4 m− 3 and US$8.2 m− 3. Fearnside (1995) obtained US$3.71 Mg C− 1 (in 1992 US dollars) on eucalyptus wood plantations under 6-year rotation in Minas Gerais

A. Bussoni, L.C.E. Rodriguez / Ecological Economics 69 (2010) 451–458

and São Paulo, Brazil, assuming project duration is 100 years and an average sequestration rate of 45 Mg C ha− 1 per rotation. The tonne of carbon value was computed dividing net present value of project cash flows into total sequestered tonne and not into marginal sequestered tonne. Proposed CDM projects in Brazil indicate US$0.47 to US$3.91 Mg C −1 for palm trees in Pará, US$5 Mg C− 1 for palm trees in Carajás and US$12.46 to US$19.94 Mg C− 1 for teak plantations in Mato Grosso. These values were obtained by dividing costs, subtracted from project establishment and maintenance, into sequestered tonne. Carbon sequestration cost estimates in Uruguay are US$39.15 Mg C− 1 in forestry projects and US$22.57 Mg C− 1 in projects combining forestry and cattle farming (Uruguay, 2002). De Jong et al. (2000) reported an average US$15 Mg C− 1 in native forests in southern Mexico. The price of the tonne of carbon varies according to changes in related prices and thus results in values that respond in an irregular manner; this is consistent with results reported by De Jong et al. (2000), who simulated variations in carbon sequestration with changes in discount rates. Discount factors differ in the way they affect revenue and costs from carbon captured in forest biomass, but no patterns could be identified for variations in accord to relative prices in projects. Generally speaking, for the proposed methodology, price variations inducing rotation age reduction for wood production regimes should result in greater differences between optimal harvest age and maximum biomass production age. Thus, the price paid per biomass unit would increase. However, comparisons of costs derived from projects using different reference periods and discount rates should be made with caution, as they may prove invalid (Feng, 2002; Richards and Stokes 2004). Site productivity as related to carbon value has scarcely been studied. Huang and Kronrad (2001) determine the optimal carbon rotation when the maximum biomass MAI is attained. In fast growth species this age could be shorter than maximum LEV. For instance, Pinus taeda at an Index Site of 27.5 m referenced to age 25 years, reaches the maximum carbon MAI of Mtonne 3.77 C at age 34 years, which is larger than the maximum LEV. Here, average productivity stands reach maximum carbon MAI of Mtonne 7.5 C at age 4–5 years and maximum LEV at age 6 years. In high-productivity stands, maximum carbon MAI Mtonne 12 C occurs at age 6 years and maximum LEV is attained at age 6.5 years. Benítez and Obersteiner (2005) suggested the agricultural rent should be included in the carbon price. If agricultural rent was lower than forestry rent, then the area would not be considered for carbon sequestration. Land units deemed suitable for carbon sequestration are only those with poor biomass growth rates or so distant from markets that profitable forestry would be an unlikely endeavor. This assumption therefore hinders the method, as it excludes areas with high forest biomass growth potential but low agricultural rent, which would be the case for extensive areas in South America, particularly in Brazil and Uruguay. Few studies include private forestry optimum value as an opportunity cost of forest companies (Díaz Balteiro and Romero, 2001; Gong and Kriström, 1998; Hoen and Solberg, 1994; Huang and Kronrad, 2001). Studies undertaken on this subject determine carbon supply through subsidy levels granted by society to forest landowners. From a CDM viewpoint, subsidy is the equivalent of how much GEG agents are willing to pay for transferring their own emission reduction goals to other agents. These in turn would be paid for maintaining a carbon stock in standing forest biomass. Nordhaus (1991) computed marginal cost of emission reduction as a percentage of baseline value. Romero et al. (1998) based their estimates on the principle of tax-subsidy. The methodology hereby proposed could be of use in defining forestry sector subsidy policies, as the gap with private optima can be determined. In that sense, this work resembles studies on systems minimizing sequestration costs through subsidies, which represent social willingness to pay in order

457

to capture GEG (Díaz Balteiro and Romero, 2001; Hoen and Solberg, 1994; Plantinga et al., 1999; Van Kooten et al., 1995). 5. Conclusions The p⁎ method proposed enables the determination of a private supply curve for net sequestered carbon and can be adapted to computing recently approved CDMs through temporary emission reduction certificates (CERt). The value of the tonne of carbon estimated, defines minimum values to be paid to forest owners, in order to induce a change in silvicultural management regimes. The model used for environmental assets valuation was shown to be adequate for value estimation in the absence of developed markets. This valuation could also be applied to subsidy estimation. Marginal carbon storage cost derived from Faustmann's formula enables the estimation of carbon supply from the opportunity cost of forestry, granting more weight to tonne sequestered early. The compensation price for maintaining forest growing stock uncut can thus be computed. Carbon sequestration price was found to be sensitive to wood price. The variables that affect the latter, such as distance to markets and production costs, may determine the supply of carbon sequestration. The study of these variables would represent a different approach, to be included in future research. The method proposed does not allow for low or zero commercial value. Among the ways to perfect p⁎ computation methods, the importance of considering postharvest wood product life expectancy is stressed. Results show that carbon supply is more cost-efficient in highly productive sites. Nevertheless, because of leakage effects, net carbon capture in the commercial plantations described is possibly nil, as reduction attained by carbon sequestration could be offset by emission from other activities. Costs found in present work are higher than values cited by literature for Brazilian markets and stock market prices. This suggests that, even though these methods may be valid, commercial plantations such as those described are unlikely as cost-effective carbon sinks if other CDMs such as energy source substitution show better performance. Acknowledgements The authors want especially to thank Ing. Agr. Juan Cabris de León for his careful review of and insightful comments on the manuscript. Gratitude is also expressed to Dr. José Luis Stape for the use of his trial for the purpose of this study. Finally we thank the anonymous reviewers for their helpful comments and corrections. References Benítez, P., Obersteiner, M., 2005. Site identification for carbon sequestration in Latin America: a grid-based economic approach. Forest Policy and Economics 8 (6), 636–651. Brown, S., Masera, O., Sathaye, J., 2000. Project-based activities. In: Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J., Dokken, D.J. (Eds.), IPCC Special Report. Land Use, Land-use Change, and Forestry. Cambridge Univ. Press, Cambridge, UK, pp. 283–338. Carvalho, A.M., Camargo, F.R.A., 1996. Variação da densidade básica entre procedências e progênies de Eucalyptus saligna. O papel. 57 (69), 56–59. CCE, 2006. Chicago Climate Exchange CCX Market Report. v.iii(1). http://www. chicagoclimateexchange.com/docs/publications/CM_060201.pdf. (1 February 2006). Commercial Property Finance Brokers, 2002. Structured finance. http://www.clpuk. com/swap_frame.htm (5 July 2009). COP9 Report of the Conference of the Parties on its Ninth Session, held at Milan, 1–12 December 2003. http://unfccc.int/resource/docs/cop9/06a02.pdf (5 July 2009). Creedy, J., Wurzbacher, A., 2001. The economic value of a forested catchment with timber, water and carbon sequestration benefits. Ecological Economics 38 (1), 71–83.

458

A. Bussoni, L.C.E. Rodriguez / Ecological Economics 69 (2010) 451–458

De Jong, B.H.J., Tipper, R., Montoya Gómez, G., 2000. An economic analysis of the potential for carbon sequestration by forests: evidence from southern Mexico. Ecological Economics 33 (2), 313–327. Díaz Balteiro, L., Romero, C., 2001. Forest management and carbon captured: analytical aspects and policy implications. Investigación Agropecuaria: Sistemas de Recursos Forestales, Fuera de Serie. 1, 153–165. Dutschke, M., Schlamadinger, B., 2003. Practical Isuues Concerning Temporary carbon credits in the CDM. Hamburg Institute of International Economics, HWWA Discussion Paper 227. ECE, 2006. European Climate Exchange. ECX EUA Futures Contract: Historic Data 2006 In tonnes of CO2 (EU Allowances). http://www.ecx.eu/index.php/EUA-Futures (5 July 2009). Faustmann, M., 1849. Berechnung des Werthes, welchen Waldboden, sowie noch nicht haubare Holzbestände für die Waldwirthschaft besitzen. Allg. Forst-u. J. Ztg. 20, 441–455. Fearnside, P., 1995. Global warming response options in Brazil's forest sector: comparison of project-level costs and benefits. Biomass and Bioenergy 8 (5), 309–322. Feng H., 2002. The dynamics of carbon sequestration and measures of costeffectiveness. Iowa State University. (Working Paper, 02-WP 320) http://www. econ.iastate.edu/research/webpapers/paper_10107.pdf (5 July 2009). Gompertz, B., 1832. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical Transactions 123, 513–585. Gong, P., Kriström, B., 1998. Regulating forest rotation to increase CO2 sequestration. In: Conference of Environmental Economics, 5., Ulvön, 1998. Anais. Ulvön: SLU. Hoen, H.F., Solberg, B., 1994. Potential and economic efficiency of carbon sequestration in forest biomass through silvicultural management. Forest Science 4 (3), 429–451. Huang, C., Kronrad, G.G., 2001. The cost of sequestering carbon on private forest lands. Forest Policy and Economics 2, 133–142. Instituto Pesquisas Econômicas—IPEA, 2003. Ipeadata. http://www.ipeadata.gov.br (5 July 2008). Karmacharya, S.B., Singh, K.P., 1992. Biomass and net production of teak plantations in a dry tropical region in India. Forest Ecology and Management 55, 233–247. Knoke, T., Weber, M., 2006. Expanding carbon stocks in existing forests—a methodological approach for coast appraisal at the enterprise level. Mitigation and Adaptation Strategies for Global Change 11, 579–605. Lewis, D.K., Turner, D.P., Winjum, J.K., 1996. An inventory-based procedure to estimate economic cost of forest management on a regional scale to conserve and sequester atmospheric carbon. Ecological Economics 16 (1), 35–49. Li, Z., Apps, M.J., Banfield, E., Kurz, W.A., 2002. Estimating net primary production of forests in the Canadian Prairie Provinces using an inventory-based carbon budget model. Canadian Journal of Forestry Research 32 (1), 161–169. Moulton, R.J., Richards, K.R., 1990. Costs of sequestering carbon through tree planting and forest management in the United States. Washington: Department of Agriculture, Forest Service, Dec. 1990. 47p. General Technical Report, 58. Nordhaus, W.D., 1991. The cost of slowing climate change: a survey. Energy Journal 12 (1), 37–65.

Paul, K.I., Polglase, P.J., Nyakuengama, J.G., 2002. Change in soil carbon following afforestation. Forest Ecology and Management 168, 241–257. Plantinga, A.J., Mauldin, T., Miller, D., 1999. An econometric analysis of the costs of sequestering carbon in forests. American Journal of Agricultural Economics 81 (4), 812–824. Richards, K., Stokes, C., 2004. A review of carbon sequestration cost studies: a dozen years of research. Climatic Change 63, 1–48. Romero, C., 1994. Economía de los Recursos Ambientales y Naturales, second ed. Alianza, Madrid. Romero, C., 1996. Multicriteria decision analysis and environmental economics: an approximation. European Journal of Operational Research 96 (1), 81–99. Romero, C., Ros, V., Díaz Balteiro, L., 1998. Optimal forest rotation age when carbon captured is considered: theory and applications. Journal of the Operational Research Society 49, 121–131. Samuelson, P.A., 1976. Economics of forestry in an evolving society. Economic Inquiry 14, 466–492. Stape, J.L., 2002. Production ecology of clonal eucalyptus plantations in northeastern Brazil. Fort Collins. 237p. Thesis (PhD)—Colorado State University. UNIT, 2006. Gestión Forestal Sostenible. Norma UNIT 1152: 2006. 19 p. http://www. unit.org.uy/catalogo/detalle.php?idC=1845&resaltar (5 July 2009). URUGUAY (2002) Ministerio de Vivienda, Ordenamiento Territorial y Medio Ambiente. Dirección de Medio Ambiente, Unidad de Cambio Climático. Estudio de apoyo a la aplicación del mecanismo para el desarrollo limpio del protocolo de Kioto en Uruguay. Montevideo, MVOTMA. 164p. Van Kooten, G.C., Binkley, C.S., Delcourt, G., 1995. Effect of carbon taxes and subsidies on optimal forest rotation age and supply of carbon services. American Journal of Agricultural Economics 77 (2), 365–374. Vital, B.R., Bastos Filho, J.G., Valente, O.F., 1985. Efeito da idade da árvore sobre o rendimento gravimétrico e teor de carbono fixo de carvão de Eucalyptus. Revista Árvore 9 (2), 180–185. WORLD BANK, 2003. Prototype Carbon Found—PCF. Prototypecarbonfund. http:// wbcarbonfinance.org/Router.cfm?Page=PCF (5 July 2009).

Glossary CDM: Clean Development Mechanisms. CERs: certified emissions reductions. lCER: long-term certified emission reductions. tCER: temporary certified emission reductions. COP9: Conference of the Parties held in Milan in 2003. GEG: Greenhouse Effect Gases. LEV: Land Expectation Value. LULUCF: Land Use, Land Use Change and Forestry. MAI: Mean Annual Increment. NPV: Net Present Value.