Annex 4 analytical methods

model description

general description

acronym: TiMBA

name: Timber market Model for policy-Based Analysis

main purpose: TiMBA (Timber market Model for policy-Based Analysis) is a python based and open access partial economic equilibrium model for the global forest products market. The model endogenously simulates production, consumption and trade of wood and wood-based products in 180 countries.

homepage: https://github.com/TI-Forest-Sector-Modelling/TiMBA

Developer and its nature

ownership: Third-party ownership (commercial companies, Member States, other organisations, ...)

ownership additional info: TiMBA was developed and written by an authors' collective named Thünen Institute Forest Sector Modelling (TI-FSM). Copyright ©, 2024, Thuenen Institute, TI-FSM, wf-timba@thuenen.de

is the model code open-source?: YES

Model structure and approach with any key assumptions, limitations and simplifications

details on model structure and approach

TiMBA calculates production, imports, exports, consumption, and prices for the forest-based sector, taking into account available forest resources, costs, technology, and trade constraints. In each period, the recursive market model consists of a static and a dynamic phase. During the static phase, TiMBA determines a global equilibrium across products and countries for a given year. The optimization problem is solved annually by maximizing economic welfare, defined as the sum of producer and consumer economic surplus. In the dynamic phase, changes in equilibrium conditions, such as shifts in parameters like growing GDP, population, or costs, are updated from one period to the next.

Forest module

The development of forest area is simulated exogenously using the environmental Kuznets curve (EKC) approach (Kuznets 1955; Grossmann and Krueger 1991). This concept describes an inverted U-shaped relationship between income development and deforestation. Initially, as GDP per capita rises, deforestation increases until it reaches a turning point. Beyond this point, further increases in GDP per capita result in a decreasing rate of deforestation (Panayotou 1993). Forest stock growth is linked to the area’s development.

Supply module

The country-specific supply of roundwood depends on wood prices and forest resources development, which is determined by the growth dynamics of the forest stock, the change in forest area, and harvested volumes.

Demand module

The country-level demand for wood-based products is governed via product-specific elasticities by income and endogenous price development.

Trade module

In TiMBA, each country imports from and exports to one World market. The level of trade depends on the trade volumes of the previous year, domestic supply and demand for a certain product as well as the international competitiveness of the respective country. Trade is further determined transport cost and other trade barriers.

Manufacturing

The production of intermediate and final products is influenced by manufacturing costs and input-output coefficients. Pre-calibrated input-output coefficients indicate the quantity of primary or intermediate inputs required to produce one unit of an intermediate or final product. Manufacturing costs are all costs of inputs except raw materials, implicitly covering expenses for labor, energy, capital, and other materials.

Price Formation

Prices are determined endogenously through market clearing at the global and national level and prices adjust to balance supply, demand, and trade, ensuring market equilibrium

model inputs

GDP and development
Population and development
Price elasticities
GDP elasticities
Forest stock elasticities
Forest area elasticity
Production quantity of all products considered Import quantity of all products considered
Export quantity of all products considered
Forest area quantity
Forest stock quantity
Ratio inventory drain to harvest
Ad valorem tax rates
Freight costs
Input / Output Coefficients (pre-calibrated)
Manufacturing costs (pre-calibrated)
Product prices (pre-calibrated)

Sources:

The FAOSTAT Database on Forestry Production and Trade (FAOSTAT 2022), along with the FAO Global Forest Resources Assessment (FRA) (FAO 2022) and the World Bank Development Indicators (World Bank 2022), are essential data sources. In its standard form, demand price and income elasticities are primarily derived from Morland et al. (2018). TiMBA incorporates GDP and population growth projections from the Shared Socioeconomic Pathways scenario “Middle of the road” (SSP2), as presented by Riahi, K et al. (2017) (These projections will be updated as soon as the data are available). Estimates for technology developments, specifically input-output coefficients and manufacturing costs, are based on historical trends from 1993 to 2020. Trade inertia and cost information is grounded in WTO data as featured in the Global Forest Products Model (Buongiorno 2015, 2021). Ad Valorem tax rates are updated according to Schier et al. (2025).

Model input data are optimized processed in a separate model approach, called model calibration. A baseline scenario with SSP2 developments starting in 2020 is delivered with the TiMBA model.

model outputs

Forest area
Forest stock
Quantity of demand, supply, import and export over 16 forest sector products
Prices of demand, supply, import and export over 16 forest sector products
Welfare
World market prices for forestry products

In addition, you may provide additional relevant information.

Intended field of application

policy role

TiMBA can offer analytical support throughout the different phases of the policy cycle in the forest sector. The model outcomes help policymakers to assess complex interactions between markets, resources, and policy interventions in a transparent and data-driven manner. TiMBA use scenario analysis, allowing policymakers to evaluate the potential impacts of different regulatory pathways or legislative proposals, which are otherwise difficult to grasp in their complexity.

During agenda setting, the model results can support the help identification of emerging challenges by projecting market trends and resource needs. In policy formulation, different model runs support the comparison of policy options by simulating their impacts on production, trade and forest resource use. TiMBA can contribute to legitimation (or non-legitimation) of policies by providing evidence-based assessment for proposed measures.

During implementation and evaluation, the analysis can guide practical decisions by highlighting possible market responses and assess whether the policy implementation would achieve intended outcomes, helping to anticipate and manage projected disruptions. For policy maintenance, succession, or termination, the model can inform strategic decisions by modeling long-term effects under evolving market conditions.

Examples:

As part of an EU project, wood-based biomass potentials were estimated using the TiMBA model and subsequently analyzed in a report focusing on future outlooks up to the year 2050. (Carus et al. 2025)

TiMBA is being applied in the Carbon Leak Project (https://www.thuenen.de/en/cross-institutional-projects/carbon-leak) to assess how climate protection measures, such as CO₂ pricing, influence the production and trade of raw and semi-finished wood products.

policy areas

Agriculture and rural development
Climate action
Taxation
Energy
Environment
Consumers
International cooperation and development
Research and innovation
Trade
Statistics
European neighbourhood policy

Model transparency and quality assurance

Are uncertainties accounted for in your simulations?: YES - Uncertainties are dealt with at different stages of the modelling process. uncertainties in input data (esp. data of production and trade) are determined and tackled during model calibration using both data smoothing and a goal-programming based optimization routine to detect and correct remaining implausibility’s. Uncertainties in input parameter such as income and price elasticity are a tried to minimize by using holistic econometric approaches to estimate them and / or fit input parameters in line with model performance. Uncertainties resulting from scenarios definitions are represented, e.g., through the use various scenarios, creating a range within which the results fluctuate.

Has the model undergone sensitivity analysis?: YES - A sampling-based approach was employed to assess sensitivity of modelling results. Input parameters were iteratively adjusted, and the resulting outputs were thoroughly examined.

Has the model been published in peer review articles?: NO

Has the model formally undergone scientific review by a panel of international experts?: NO

Has model validation been done? Have model predictions been confronted with observed data (ex-post)?: YES - The model underwent a thorough validation process, which included comparisons with historical data, evaluations against other forest sector models, and a stress test.

To what extent do input data come from publicly available sources?: Entirely based on publicly available sources

Is the full model database as such available to external users?: YES - It is based on Git Hub open source with comprehensive read me, developed in Python

Have model results been presented in publicly available reports?: YES

Have output datasets been made publicly available?: NO

Is there any user friendly interface presenting model results that is accessible to the public?: NO

Has the model been documented in a publicly available dedicated report or a manual?: YES

Intellectual property rights

Licence type: Free Software licence

application to the impact assessment

Please note that in the annex 4 of the impact assessment report, the general description of the model (available in MIDAS) has to be complemented with the specific information on how the model has been applied in the impact assessment.

See Better Regulation Toolbox, tool #11 Format of the impact assessment report).