Our Common Future under Climate Change (Cfcc), Paris, France, 7-10 July 2015

A bioeconomic modelling of logged tropical forests to simulatelow-carbon strategies for Central African concessions

Florian Claeys1,2,3,4,?, Philippe Delacote3,4,5, Sylvie Gourlet-Fleury2, Alain Karsenty2, Frédéric Mortier2

1 Engref, AgroParisTech, Paris, France ; 2 Bsef, Cirad, Montpellier, France ; 3 Lef, AgroParisTech, Nancy, France ; 4 Umr 356 Forest Economics, Inra, Nancy, France ; 5 Cec,University of Paris-Dauphine – Cdc Climat, Paris, France. ? Corresponding author : florian.claeys@cirad.fr.

Short context

Within Redd+ context, Improved forest management (Ifm) refers to any change of practice in forest harvesting that enables to generate a carbon benefit (Putz et al. 2012,Somorin et al. 2012, Griscom and Cortez 2013). Ifm activities are of major importance in the Congo Basin forests, where 38 % of the 50 Mha of conceded forest lands arecurrently covered by a sustainable management plan (Bayol et al. 2014). Among Ifm projects (Vcs 2013), the "extension of rotation age" (Era) projects aim to reduce emissionsby increasing minimum cutting diameters (Mcd) and/or extending felling cycle duration (Fcd). However, such activities have negative consequences for the profitability oftimber companies. Climate instruments such as the mechanism of "Reducing emissions from deforestation and forest degradation and the role of conservation, sustainablemanagement of forests and enhancement of forest carbon stocks in developing countries" (Redd+) promote a compensatory approach to cover these income losses by thevaluation of avoided carbon emissions (Karsenty et al. 2012). To elucidate the extent to which carbon valuation can compensate logging companies’ loss, we developed abioeconomic approach coupling a mixture of inhomogeneous matrix models (Mimm) for forest dynamics and an object-oriented model for logging companies’ operations.Based on a unique 30-years-long monitoring of a Central African forest, we predicted the evolution of the carbon stock in a forest concession for several Era scenarios and fora time scale of 100 years. We then calculated the break-even price (Bep) of carbon credits that would enable to compensate logging companies’ loss.Key words : tropical forest, "extension of rotation age/cutting cycle" (Era) projects, Redd+, bioeconomic modelling, carbon credit

Modelling methodology

M’Baïki (Gourlet-Fleury et al. 2013)

IDatasetICentral African

Republic (Car)I 6×4 ha : BoukokoI 4×4 ha : La Lolé

I 3 treatmentsI ControlI LoggingI Logging and

thinningI 30-year

follow-upI 239 speciesI 37 539 treesI 639 815

measures

Forest dynamics modelling

a) Hawthorne (1995) guilds.

b) Basal area recovery after logging.

IMimm (Mortier et al.2014)IMatrix modelI Usher (1966; 1969)

I Species clusteringbased on responsesIOuédraogo et al. (2013)

I Variables selection foreach groupIMonni and Tadesse (2009)

ITwo stepsI Adaptive LassoI Icl

IModelling validationa) Group ecological traitsb) Post-logging behaviour

Logging company modellingForest resources

SpeciesDiametersQuality

Logging choices

Commercial speciesCutting diametersFelling cycle

22 ; 30 ans ; 80 cm

Standing timber

Area

247000 ha

Cutting

CapacityYield

12400 trees.yr−1 ; 100 %

Logyard

Sawmill

CapacityYield

0.7.106 m3.yr−1 ; 30 %

Outputs yard

Kilns

CapacityYield

60.103 m3.yr−1 ; 80 %

Export yard

imber income

IHarvesting and processing ratesI Logging intensity determinationI Ranking of trees by log-equivalent valueIMerit-order maximisation of timber income

Feasibility of Era projects, based on simulation results

Simulation protocol

IReference stateIM’Baïki control plots in 2012I {30 yrs;80 cm} ; 1.5 trees.ha−1.an−1

IEra projectsI Fcd : [30 ;60] yrsIMcd : [80 ;130] cm

IBreak-even analysisICrediting period : 100 yrsIMargin rate : 10 %I Vcu buffer : 20 %IDiscount rate : 10 %

IUsed databasesI Volume equations (Ndjondo et al. 2014)I Export prices (Itto 2014)I Logging damages (Picard et al. 2012)

Carbon gain

I Two different effectsIMcd and Fcd : positive

influenceIMcd : long-termI Fcd : short-term

I ExplanationIMcd only binding at long

term

Timber income losses

{30 yrs;80 cm} {30 yrs;120 cm} {60 yrs;80 cm} {60 yrs;120 cm}

Break-even price of carbon credits

I Range : 4.7 - 9 e.Vcu−1

ICurrent prices :4.5 e (Goldstein et al.2014)

I 3 patterns in Fcd-McdspaceI Threshold effect aroundMcd= 90 cm

INegative influence ofdiscount rate on BepIMismatch between public

and private rankings bycost-effectiveness

Discussion key messages

I Logging lowers the levels of natural carbon accumulation in Central African forests.INo logging scenario prevents the collapse of timber income after the first felling cycles.IUnder current state of voluntary carbon markets, Era projects would be feasible but solely

due to the unbinding nature of logging constraints at short term.I Financing Era projects by permanent carbon credits would have major drawbacks of

acceptability and sustainability over time.

Acknowledgements

We thank the Forestry Research Support (Arf) Project and its seven partners : French Development Agency (Afd), Centre for International Cooperation in Agricultural Research for Development(Cirad), Car Institute of Agricultural Research (Icra), Ministry of Waters, Forests, Hunting and Fisheries (Mefcp) of Central African Republic, Service of Cooperation and Cultural Action (Scac) ofFrench Ministry of Foreign Affairs (Mae), University of Bangui and Car Company of Wood Peeling (Scad) for providing access to the site and to the database of M’Baïki. We are grateful to LaurentCerbonney, Émilien Dubiez, Hervé Moinecourt, François Lanckriet and all previous volunteers appointed by the Scac of Mae and the fieldworkers who participated in the project management,data collection and data capture.The Laboratory of Forest Economics contributes to the Labex Arbre ANR-11-LABX-0002-01.

References

N. Bayol, F. Anquetil, C. Bile, A. Bollen, M. Bousuqet, B. Castadot, P. Cerutti, J. Avit Kou-gape, M. Leblanc, H. Lescuyer, Q. Meunier, E. Melet, A. Penelon, V. Robligio, R. Tsanga,and C. Vautrin. In C. De Wasseige, D. Louppe, F. John, K. Heiner, B. Bedoret, D. de Beauf-fort, and C. Halleux, editors, pages47–66.Weyrich Édition, Weyrich, Belgique, 2014.

A. Goldstein, G. Gonzalez, and M. Peters-Stanley. Forest Trends’ Ecosystem Marketplace,2014.

S. Gourlet-Fleury, F. Mortier, A. Fayolle, F. Baya, D. Ouédraogo, F. Bénédet, and N. Picard.Philosophical Transactions of the Royal Society B : Biological Sciences, 368(1625) :20120302, 2013.

B. W. Griscom and R. Cortez. Tropical Conservation Science, 6(3), 2013.W. Hawthorne. Tropical Forestry Papers, 29, 1995.Itto. Tropical timber market report, 18(8), 2014. International Tropical Timber Organi-

zation (Itto), Yokohama, Japan.A. Karsenty, N. Tulyasuwan, Global Witness, and D. Ezzine de Blas. Cirad, Montpellier,

2012. Report for the European Commission DG Climate Action.S. Monni and M. G. Tadesse. Bayesian Analysis, 4(3) :413–436, 2009.F. Mortier, D.-Y. Ouédraogo, F. Claeys, M. G. Tadesse, G. Cornu, F. Baya, F. Benedet, V. Frey-

con, S. Gourlet-Fleury, and N. Picard. Environmetrics, 2014.M. Ndjondo, S. Gourlet-Fleury, R. Manlay, N. Engone Obiang, A. Ngomanda, C. Romero,

F. Claeys, A. Karsenty, and N. Picard. Carbon balance and management, 9(4), 2014.D.-Y. Ouédraogo, F. Mortier, S. Gourlet-Fleury, V. Freycon, and N. Picard. Journal of Eco-logy, 101(6) :1459–1470, 2013.

N. Picard, S. Gourlet-Fleury, and É. Forni. Canadian Journal of Forest Research, 2012.F. Putz, P. Zuidema, T. Synnott, M. Peña-Claros, M. Pinard, D. Sheil, J. Vanclay, P. Sist,

S. Gourlet-Fleury, B. Griscom, J. Palmer, and R. Zagt. Conservation Letters, 5(4) :296–303, 2012.

O. A. Somorin, H. C. P. Brown, I. J. Visseren-Hamakers, D. J. Sonwa, B. Arts, and J. Nkem.Global Environmental Change, 22(1) :288–298, 2012.

M. Usher. Journal of Applied Ecology, pages355–367, 1966.M. Usher. Biometrics, pages309–315, 1969.Vcs. Verified Carbon Standard, 2013.