Our Common Future under Climate Change (Cfcc), Paris, France, 7-10 July 2015 A bioeconomic modelling of logged tropical forests to simulate low-carbon strategies for Central African concessions Florian Claeys 1,2,3,4,? , Philippe Delacote 3,4,5 , Sylvie Gourlet-Fleury 2 , Alain Karsenty 2 , Frédéric Mortier 2 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 : fl[email protected]. 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 are currently 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 emissions by increasing minimum cutting diameters (Mcd) and/or extending felling cycle duration (Fcd). However, such activities have negative consequences for the profitability of timber companies. Climate instruments such as the mechanism of "Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries" (Redd+) promote a compensatory approach to cover these income losses by the valuation of avoided carbon emissions (Karsenty et al. 2012). To elucidate the extent to which carbon valuation can compensate logging companies’ loss, we developed a bioeconomic 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 for a 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) I Dataset I Central African Republic (Car) I 6 × 4 ha : Boukoko I 4 × 4 ha : La Lolé I 3 treatments I Control I Logging I Logging and thinning I 30-year follow-up I 239 species I 37 539 trees I 639 815 measures Forest dynamics modelling a) Hawthorne (1995) guilds. b) Basal area recovery after logging. I Mimm (Mortier et al. 2014) I Matrix model I Usher (1966; 1969) I Species clustering based on responses I Ouédraogo et al. (2013) I Variables selection for each group I Monni and Tadesse (2009) I Two steps I Adaptive Lasso I Icl I Modelling validation a) Group ecological traits b) Post-logging behaviour Logging company modelling Forest resources Species Diameters Quality Logging choices Commercial species Cutting diameters Felling cycle 22 ; 30 ans ; 80 cm Standing timber Area 247000 ha Cutting Capacity Yield 12400 trees.yr -1 ; 100 % Logyard Sawmill Capacity Yield 0.7.10 6 m 3 .yr -1 ; 30 % Outputs yard Kilns Capacity Yield 60.10 3 m 3 .yr -1 ; 80 % Export yard imber income I Harvesting and processing rates I Logging intensity determination I Ranking of trees by log-equivalent value I Merit-order maximisation of timber income Feasibility of Era projects, based on simulation results Simulation protocol I Reference state I M’Baïki control plots in 2012 I {30 yrs; 80 cm} ; 1.5 trees.ha -1 .an -1 I Era projects I Fcd : [30 ; 60] yrs I Mcd : [80 ; 130] cm I Break-even analysis I Crediting period : 100 yrs I Margin rate : 10 % I Vcu buffer : 20 % I Discount rate : 10 % I Used databases I Volume equations (Ndjondo et al. 2014) I Export prices (Itto 2014) I Logging damages (Picard et al. 2012) Carbon gain I Two different effects I Mcd and Fcd : positive influence I Mcd : long-term I Fcd : short-term I Explanation I Mcd 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 I Current prices : 4.5 e (Goldstein et al. 2014) I 3 patterns in Fcd-Mcd space I Threshold effect around Mcd= 90 cm I Negative influence of discount rate on Bep I Mismatch between public and private rankings by cost-effectiveness Discussion key messages I Logging lowers the levels of natural carbon accumulation in Central African forests. I No logging scenario prevents the collapse of timber income after the first felling cycles. I Under 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) of French 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 Laurent Cerbonney, 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.