This module handles the production of timber using plantations 32_forestry and natural vegetation 35_natveg. Timber can be produced from both commercial plantations and natural forests. The module feeds vm_prod at cluster level to 17_production and 21_trade modules. This module also calculates the “real” harvested area in natural forests i.e. v35_hvarea_primforest,v35_hvarea_secdforest and v73_hvarea_other.
| Description | Unit | A | |
|---|---|---|---|
| im_gdp_pc_ppp_iso (t_all, iso) |
Per capita income in purchasing power parity | \(USD_{05PPP}/cap/yr\) | x |
| im_pop_iso (t_all, iso) |
Population | \(10^6/yr\) | x |
| pm_representative_rotation (t_all, i) |
Representative regional rotation | \(1\) | x |
| sm_fix_SSP2 | year until which all parameters are fixed to SSP2 values | \(year\) | x |
| vm_prod (j, k) |
Production in each cell | \(10^6 tDM/yr\) | x |
| vm_prod_forestry (j, kforestry) |
Production of woody biomass from commercial plantations | \(10^6 tDM/yr\) | x |
| vm_prod_natveg (j, land_natveg, kforestry) |
Production of woody biomass from natural vegetation | \(10^6 tDM/yr\) | x |
| Description | Unit | |
|---|---|---|
| pm_demand_ext (t_ext, i, kforestry) |
Extended demand for timber beyound simulation | \(10^6 tDM/yr\) |
| pm_demand_forestry_future (i, kforestry) |
Future forestry demand in current time step | \(tDM/yr\) |
| sm_wood_density | Representative wood density based on IPCC | \(tDM/m3\) |
| vm_cost_timber (i) |
Actual cost of harvesting timber from forests | \(10^6 USD/yr\) |
biomass_mar20 realization acts as a common tunnel for land related decisions in forestry 32_forestry and natveg 35_natveg modules and corresponding production of woody biomass realized. This realization harvests timber from available plantations to meet a portion of overall timber demand. Rest of the timber production comes by harvesting natural vegetation. Aggregated timber demand for wood and woodfuel is calculated based on demand equation from Lauri et al. (2019) and income elasticities from Morland et al. (2018). This realization can also account for construction wood demand based on Churkina et al. (2020) which is added on top of industrial roundwood demand.
Timber production cost include the cost for producing wood, woodfuel and residues, as well as additional costs for harvesting (see below) and technical costs for a slack variable (‘v73_prod_heaven_timber’). The slack variable (high costs) is only used as a last resort when there is no other way to meet the timber demand. The purpose of the slack variable is to assure technically feasibility of the model under all conditions and to indicate shortage of wood supply, if any.
\[\begin{multline*} vm\_cost\_timber(i2) = \sum_{cell(i2,j2)} vm\_prod(j2,"wood") \cdot s73\_timber\_prod\_cost\_wood + \sum_{cell(i2,j2)} vm\_prod(j2,"woodfuel") \cdot s73\_timber\_prod\_cost\_woodfuel + \sum_{cell(i2,j2)} v73\_prod\_residues(j2) \cdot s73\_reisdue\_removal\_cost + \sum_{cell(i2,j2),kforestry}\left( v73\_prod\_heaven\_timber(j2,kforestry) \cdot s73\_free\_prod\_cost\right) \end{multline*}\]
The following equations describes cellular level production (in dry matter) of woody biomass vm_prod_reg as the sum of the cluster level production of timber coming from ‘v73_prod_forestry’ and ‘v73_prod_natveg’. When production capabilities are exhausted, the model can produce roundwood without using any land resources but by paying a very high cost (‘s73_free_prod_cost’). Timber production equation is split in two parts, one each for industrial roundwood and wood fuel production. Woodfuel production, in addition to usual production channels, can also use residues left from industrial roundwood harvest for meeting overall wood fuel production targets.
\[\begin{multline*} vm\_prod(j2,"wood") = vm\_prod\_forestry(j2,"wood") + \sum_{land\_natveg}vm\_prod\_natveg(j2,land\_natveg,"wood") + v73\_prod\_heaven\_timber(j2,"wood") \end{multline*}\]
\[\begin{multline*} vm\_prod(j2,"woodfuel") = vm\_prod\_forestry(j2,"woodfuel") + \sum_{land\_natveg}vm\_prod\_natveg(j2,land\_natveg,"woodfuel") + v73\_prod\_residues(j2) + v73\_prod\_heaven\_timber(j2,"woodfuel") \end{multline*}\]
Production of residues is calculated based on s73_residue_ratio. This fraction of industrial roundwood production is assumed to be lost during harvesting processes. USDA reports that ca. 30% of roundwood harvested are residues (Oswalt et al. (2019)). Not all of this residue is recoverwed from forest and we assume 50% of residue removal based on Pokharel et al. (2017). These numebrs (residue levels and residude removals vary strongly among different studies, the numbers used here are from a USDA report on state of forests in USA which has consistent reporting over years)
\[\begin{multline*} v73\_prod\_residues(j2) \leq vm\_prod(j2,"wood") \cdot s73\_residue\_ratio \end{multline*}\]
Limitations Timber demand cannot be determined endogenously
| Description | Unit | A | |
|---|---|---|---|
| f73_construction_wood_demand (t_all, i, pop_scen09, build_scen) |
Construction wood demand | \(10^6 tDM\) | x |
| f73_demand_modifier (t_ext, scen_73) |
Factor diminishing paper use | \(1\) | x |
| f73_income_elasticity (total_wood_products) |
Income elasticities of wood products | \(1\) | x |
| f73_prod_specific_timber (t_all, iso, total_wood_products) |
End use timber product demand | \(10^6 m3/yr\) | x |
| f73_regional_timber_demand (t_all, i, total_wood_products) |
End use timber product demand | \(10^6 m3/yr\) | x |
| f73_volumetric_conversion (kforestry) |
Volumetric conversion from mio t to m3 of wood | \(1\) | x |
| p73_demand_calib (t_all, i, kforestry) |
Additive calibration for timber demand | \(10^6 m3/yr\) | x |
| p73_demand_constr_wood (t_all, i) |
Demand for construction wood | \(10^6 tDM/yr\) | x |
| p73_demand_modifier (t_all) |
Simple demand modifier for construction wood | \(10^6 tDM/yr\) | x |
| p73_forestry_demand_prod_specific (t_all, iso, total_wood_products) |
End product specific timber demand | \(10^6 m3/yr\) | x |
| p73_fraction (t_all) |
Fraction over which construction wood demand is spread out | \(1\) | x |
| p73_fraction_sm_fix | Modifier fraction at sm_fix_SSP2 time step | \(1\) | x |
| p73_glo_wood (t_all, kforestry) |
Global wood demand | \(10^6 tDM/yr\) | x |
| p73_timber_demand_gdp_pop (t_all, i, kforestry) |
Timber demand based on lauri et al 2019 | \(10^6 m3/yr\) | x |
| q73_cost_timber (i) |
Actual cost of harvesting timber from forests | \(10^6 USD/yr\) | x |
| q73_prod_residues (j) |
Production of residues from industrial roundwood harvest | \(10^6 tDM/yr\) | x |
| q73_prod_wood (j) |
Production of industrial roundwood | \(10^6 tDM/yr\) | x |
| q73_prod_woodfuel (j) |
Production of wood fuel | \(10^6 tDM/yr\) | x |
| s73_expansion | Construction wood demand expansion factor by end of century based on industrial roundwood demand as base | \(1=100 percent increase\) | x |
| s73_foresight | Boolean switch for establishment demand assumption 1=forward looking 0=myopic | \(1\) | x |
| s73_free_prod_cost | Very high cost for using non existing land for plantation establishment | \(USD/tDM\) | x |
| s73_increase_ceiling | Limiter for not allowing a demand jump between time steps beyond a certain limit | \(1\) | x |
| s73_reisdue_removal_cost | Cost of removing residues left after industrial roundwood harvest | \(USD/tDM\) | x |
| s73_residue_ratio | Proportion of overall industrial roundwood production which ends up as residue during harvest | \(1\) | x |
| s73_timber_demand_switch | Logical switch to turn on or off timber demand 1=on 0=off | \(1\) | x |
| s73_timber_prod_cost_wood | Cost for producing one unit of wood | \(USD/tDM\) | x |
| s73_timber_prod_cost_woodfuel | Cost for prodcing one unit of woodfuel | \(USD/tDM\) | x |
| v73_prod_heaven_timber (j, kforestry) |
Production of woody biomass from heaven | \(10^6 tDM/yr\) | x |
| v73_prod_residues (j) |
Production of residues from industrial roundwood harvest | \(10^6 tDM/yr\) | x |
| description | |
|---|---|
| build_scen | Building wood scenario |
| cell(i, j) | number of LPJ cells per region i |
| construction_wood(total_wood_products) | Wood products used for building construction |
| i | all economic regions |
| i_to_iso(i, iso) | mapping regions to iso countries |
| i2(i) | World regions (dynamic set) |
| iso | list of iso countries |
| j | number of LPJ cells |
| j2(j) | Spatial Clusters (dynamic set) |
| k(kall) | Primary products |
| kforestry_to_woodprod(kforestry, total_wood_products) | Mapping between intermediate and end use wood products |
| kforestry(k) | forestry products |
| land | Land pools |
| land_natveg(forest_land) | Natural vegetation land pools |
| pop_scen09 | Population scenario |
| scen_73 | Forestry future scenario |
| t_all(t_ext) | 5-year time periods |
| t_ext | 5-year time periods |
| t_past_forestry(t_all) | Forestry Timesteps with observed data |
| t(t_all) | Simulated time periods |
| total_wood_products | End use wood product category from FAO |
| type | GAMS variable attribute used for the output |
| wood_panels(wood_products) | Wood products used for panels construction |
| wood_products(total_wood_products) | Major 2nd level products from wood processing |
Abhijeet Mishra, Florian Humpenöder
09_drivers, 11_costs, 16_demand, 17_production, 32_forestry, 35_natveg
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Lauri, Pekka, Nicklas Forsell, Mykola Gusti, Anu Korosuo, Petr Havlík, and Michael Obersteiner. 2019. “Global Woody Biomass Harvest Volumes and Forest Area Use Under Different Ssp-Rcp Scenarios.” Journal of Forest Economics 34 (3-4): 285–309. https://doi.org/10.1561/112.00000504.
Morland, Christian, Franziska Schier, Niels Janzen, and Holger Weimar. 2018. “Supply and Demand Functions for Global Wood Markets: Specification and Plausibility Testing of Econometric Models Within the Global Forest Sector.” Forest Policy and Economics 92: 92–105.
Oswalt, Sonja N, W Brad Smith, Patrick D Miles, and Scott A Pugh. 2019. “Forest Resources of the United States, 2017: A Technical Document Supporting the Forest Service 2020 Rpa Assessment.” Gen. Tech. Rep. WO-97. Washington, DC: US Department of Agriculture, Forest Service, Washington Office. 97.
Pokharel, Raju, Robert K Grala, Donald L Grebner, and Stephen C Grado. 2017. “Factors Affecting Utilization of Woody Residues for Bioenergy Production in the Southern United States.” Biomass and Bioenergy 105: 278–87.