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Accounting for socio-economic drivers in climate change analysis with the
French Forest Sector Model 2.0
Ahmed Barkaoui1, Sylvain Caurla1, Philippe Delacote1, Antonello Lobianco2
1. Laboratoire d'Economie Forestière (LEF), Nancy, France - 2. Università Politecnica delle Marche, Ancona, Italy
Objectives
Given the importance of anthropogenic determinants in forest growth within Europe, the objective of this work is to link the evidence arising from biological models of climatically-induced variations in forest ecosystems with socio-economic determinants, where the expected returns of forest investments represent the main drivers.
Hence first an adequate spatial scale is adopted to facilitate models linkage and then a management module is introduced to account for expected climatically-induced variations in forest investments.
With both climate and economic drivers considered we can try to: understand the impact of forward-looking vs conservative forest management; forecast the impact of climate change on forest profitability, depending on forest managers strategies; forecast the long-term evolution of the French land-use, comparing forests and agriculture profitability.
Implementation
FFSM 2.0 is composed of tree modules: the market module (KM) is a partial equilibrium model that determines wood market prices, demand, supply and trade, the resource module (RM) simulates the forest dynamics, and the management module (GM) determines investments in a specific forest type.
While a regional scale is reasonably adequate for KM, it is not for RM and GM: hence an high-scale, GIS based approach has been implemented for RM and GM where climatically-induced variations produced from the (exogenous) biological models can be accounted at a pixel level.
The GM module endogenises forest wood regeneration (depending on harvesting levels), incorporates forest managers expectations and finally determines model forest investments (replanting).
The unmanaged forest area is treated according to a probability of presence (of forest species) derived from the biological models.
Development status
FFSM 2.0 is currently under validation. Exercises have been performed in order to empirically validate the new C++ coded model (FFSM 2.0) in comparison against its GAMS-coded predecessor (FFSM 1.0 - this is a well established model that has already been used in several exercises of political economy simulations) and the effects on the model of considering endogenous regeneration rather than fixed (exogenous) regeneration.
More extensive validation exercises are on the way.
Forest
Resources
Economic
module
resource level information
harvesting
exogenous regeneration
exogenous growth
Forest
Resources
Economic
module
resource level information
harvesting
growth
future growth expectations
Climate
scenario
Management
module
price expectations
harvested area
regeneration
( FFSM 1.0 )
( FFSM 2.0 )
information flow
physical flow
At a glance
Module
Spatial Levels
Var Input
Var Output
Market
Countries, regions
Invr,pp,t, Pricewrd,pp,t
Supplyr,pp,t, Pricer,pp,t
Resource
Counties, regions, pixels
Supplyr,pp,t, RegAreapx,ft,t
Invpx,pp,t+1, HAreapx,ft,t
Management
Countries, regions, pixels
Pricer,pp,t, HAreapx,ft,t
RegAreapx,ft,t
Expected returns from a given forest type depend from both market price of related wood products and biological parameters;
converted to annualised valued ;
each possible combination is multiplied by a weighting factor of the actual harvested for that specific combination to the total harvested for the forest type (only final harvested considered).
The regeneration area for each forest type is equal to the unmanaged share of the harvested area of its own type ;
the managed share of the total harvested area is allocated as the regeneration area of the forest type having the highest expected return ( ).
A time leg exists between harvesting/regeneration and availability of wood resources
Forest investments and regeneration
Each year an harvested area for each f.t. is computed from the harvested volumes (in turn derived from market demand)
Model dynamic
Level 1
(countries)
Level 2
(regions)
Pixel
level
Climate
scenario
FFSM
Management
and resource
modules
Forest growth
models
pixel level
pixel level
pixel level
Spatial Representation
Decoupling the spatial scale of the market module (regional) from those of the resource and management modules (pixel)
regional scale reasonably adequate for the market module
Consistency within ORACLE projects:
t
time
[2005-2100]
c
country
{France}
r
region
[22 administrative regions in France]
px
pixel
sp
forest species group
{Broadleaves, Coniferous}
mt
forest management type
{High forests, Mixed forests, Coppers}
ft
forest type (including management: sp × mt, e.g. coppers broadleaved or high forest coniferous)
dc
diameter class
{0, 15, 25, 35, 45, 55, 65, 75, 85, 95, 150}
pp
primary product (that is, deriving directly from forest resources)
{Hardwood Roundwood, Softwood Roundwood, Pulpwood and Fuelwood}
tp
transformed products
{Fuelwood, Hardwood Sawnwood, Softwood Sawnwood, Plywood, Pulpwood, Pannels}
prd
products (pp È tp)
equation indices
Caurla S., Delacote P., Lecocq F., Barkaoui A. (2009). Fuelwood Consumption, Uncertainty over Resources and Public Policies: What Impacts on the French Forest Sector? Cahier du LEF n°2009-03, Nancy, 24 p.
Caurla S., Lecocq F., Delacote P., Barkaoui A. (2010). The French Forest Sector Model version 1.0. Presentation and theoretical foundations. Cahier du LEF n°2010-03, Nancy, 41 p.
Caurla, S., Delacote, P., Lecocq, F., Barkaoui, A. (2013). Combining an intersectorial carbon tax with sectorial mitigation policies: which impacts on the French Forest Sector? Forthcoming in Journal of Forest Economics.
Delacote P., Lecocq F. (2011). Fuelwood, Timber and Climate: Insights from forest sector modeling- An introduction, Journal of Forest Economics, 17(2), 107-109.
Lecocq F., Delacote P., Caurla S., Barkaoui A., Sauquet A. (2011). Paying for forest carbon or stimulating fuelwood demand? Insights from the French Forest Sector Model. Journal of Forest Economics, 17(2), 157-168.
Sauquet, A., Lecocq, F., Delacote, P., Caurla, S., Barkaoui, A., Garcia, S. (2011) Estimating Armington elasticities for sawnwood and application to the French Forest Sector Model. Resource and Energy Economics 33(4), 771-781.
Wernsdörfer, H., Colin, A., Bontemps, J-D., Chevalier, H., Caurla, S., Pignard, G., Leban, J-M., Hervé, J-C., and Fournier, M. (2012). Large scale dynamics of a heterogeneous forest resource are jointly driven by geographically varying growth conditions, tree species composition and stand structure. Annals of Forest Sciences, 69(7), 829-844.
FFSM references
further information
http://www7.nancy.inra.fr/lef/recherches/ffsm