TiMBA - Timber market Model for policy-Based Analysis

TiMBA is a 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. TiMBA recursively computes the market equilibrium for each country and product in a given period by maximizing the social surplus in the global forest sector. In the equilibrium processes, product supply, demand and price are balanced for each simulation period.

TiMBA - Timber market Model for policy-Based Analysis

1. Cite TiMBA

We are happy that you use TiMBA for your research. When publishing your work in articles, working paper, presentations or elsewhere, please cite the model as

TI-FSM, Morland, C., Schier, F., Tandetzki, J., Honkomp, T. (2025). TiMBA (Timber market Model for policy-Based Analysis). Journal of Open Source Software, 10(115), 8034, https://doi.org/10.21105/joss.08034
Download BibTeX

The authors' collective is named "Thünen Institute Forest Sector Modelling (TI-FSM)". The individual authors are listed as Co-authors in alphabetical order.

2. Installation

The package is developed and tested with Python 3.9 on Windows 11. TiMBA is compatible with Python versions between 3.9 - 3.11. The functionality with supported Python versions is continuously tested using GitHub CI with operating system images windows-latest and ubuntu-latest.

2.1 Requirements

Operating system: Linux or Windows on a x64 architecture. We test Windows 11 and Ubuntu 24.04 but other Windows and Linux versions are likely to work, too.
The Python package manager uv. We recommend to use uv, because it provides a user-friendly, quick, platform-independent, and reliable way to install, use, and develop TiMBA.

That said, TiMBA can be also installed classically by creating a virtual environment using venv and installing with pip. If your system Python doesn't happen to be version 3.9 - 3.11, you need to install a supported Python version to make the virtual environment use.

e.g. python3.11 -m venv .venv.

Known Issues: TiMBA currently does not work with Python 3.12 or higher. We observe numerical discrepancies (>5% compared to results generated on Windows or Linux) when running TiMBA on MacOS which could traced back to the solver OSQP in CVXPY. The results with MacOS have not been validated. We are investigating the issue.

2.2 Install uv

2.2.1 Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

2.2.2 Windows

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2.3 Install the TiMBA command line programs

If you want to use TiMBA as executable program, install it globally like so:

uv tool install -p 3.11 pytimba

You will then have access to the CLI command timba with the subcommands carbon, dashboard, load ans run. Get an overview with timba --help. Detailed usage instructions are available with the argument --help for each of the subcommands. See section Model settings for further details.

2.3.1 Troubleshooting: `uv` command not found

If you encounter the following error message:

'uv' is not recognized as an internal or external command,
operable program or batch file.

uv may be installed successfully, but the installation directory is not available in your PATH.

Check the installation output for the line:

installing to <path>

Add this path to your environment variable PATH, e.g. on Windows like so:

set PATH=%PATH%;<path>

For a permanent solution, add <path> to your user PATH environment variable in Windows.

2.4 Install TiMBA from PyPi

If you want to use TiMBA as part of your own program, you can install it from PyPi like so:

uv init -p 3.11 my_timba_program  # Create a project skeleton with Python 3.11
cd my_timba_program               # Change into the project directory
uv add pytimba                    # Install TiMBA into the project's virtual environment.

Use TiMBA in Python modules, for example

from TiMBA.main import run_timba

run_timba()

To change the folder for input and output data, the user can use the folderpath option (note: the path must be a Path object from pathlib):

from TiMBA.main import run_timba
from pathlib import Path

run_timba(folderpath=Path(r"your_path"))

To modify specific parameters, you can import parameter_setter from TiMBA.main and set new values:

from TiMBA.main import run_timba, parameter_setter

parameters = parameter_setter()
parameters.max_period = 2

run_timba(Parameters=parameters)

2.5 Install TiMBA from GitHub

To install TiMBA in editable mode, so that you can modify the TiMBA source code, install it directly from GitHub:

Clone the repository and move into the project folder:

git clone https://github.com/TI-Forest-Sector-Modelling/TiMBA.git timba
cd timba

Create the virtual environment and install all dependencies into it:
```
uv sync
```
Verify the installation and run a first period:
```
uv run timba run --help
uv run timba run -MP=1
```

2.5.1 Install from GitHub without 'uv`

If you want to develop without using uv, you can install TiMBA with venv and pip, for example for Windows like so:

The prerequisite is that a supported version (3.9 -3.11) is among your system-wide installed Python interpreters and that you have Python Launcher for Windows installed.

Clone the repository and move into the project folder:

git clone https://github.com/TI-Forest-Sector-Modelling/TiMBA.git timba
cd timba
Create a virtual environment Select the correct Python interpreter.
Show installed versions:

py -0
- If you have installed multiple versions of Python, activate the correct version using the py-Launcher.
py -3.11 -m venv venv
- If you are using only a single version of Python on your computer:
python -m venv venv
Activate the virtual environment
Enable the virtual environment to isolate TiMBA dependencies.

venv\Scripts\activate
Install TiMBA in the editable mode

pip install -e .

If the following error occurs: "ERROR: File "setup.py" or "setup.cfg" not found." you might need to update the pip version you use with:

python.exe -m pip install --upgrade pip

2.6. Testing TiMBA

The TiMBA model comes with a test suite to ensure its functionality. Run the test suite to check the functionality of the package and validate the produced results with those provided by the TI-FSM using the coverage report:

uv run python -W ignore::DeprecationWarning -m coverage run --rcfile=.coveragerc -m unittest discover -s test -p "test*.py"
uv run coverage report

To reduce the test suite running time, only the first period will be computed and compared. The test suite results will not be saved.

3. Use TiMBA

TiMBA provides a built-in command-line interface (CLI) that enables users to download input data, execute simulations, and analyse model outputs.

To run TiMBA, a scenario input file together with the required auxiliary datasets (e.g. country and commodity information) is required. The software package intentionally does not include any default datasets. Instead, software and data are maintained separately, allowing datasets to be versioned, cited, and updated independently.

The default input datasets are archived on Zenodo in the repository TiMBA Additional Information. They can be downloaded directly via the CLI:

timba load

If no target directory is specified by the user, the datasets are downloaded into the current working directory. The command retrieves the latest public release of the TiMBA default scenario. Additional scenario datasets will be published separately alongside the corresponding scientific publications and then can be loaded separately.

The main entry point for executing simulations is:

timba run

If the required input data are not available locally, TiMBA automatically downloads the default dataset before starting the simulation. Consequently, an internet connection is required for the first execution.

TiMBA reads the scenario input file (scenario_input.xlsx) together with all required auxiliary data. A detailed description of the input data structure is provided in the section TiMBA extended model description.

Although TiMBA can also be imported directly as a Python package, the CLI is the recommended interface for most users.

The available CLI commands are:

Command	Description
`load`	Download input datasets from the TiMBA data repository.
`run`	Execute TiMBA simulations.
`carbon`	Calculate forest carbon stocks in forests and harvested wood products.
`dashboard`	Launch an interactive dashboard for analysing simulation results.

By default, TiMBA uses the current working directory also for output files. A different project directory can be specified using the -FP (folder path) option:

timba run -FP your_path

This allows users to organise multiple projects or customise model runs with alternative input datasets (see Model settings for further details).

4. Supplementary modules

The TiMBA ecosystem has a modular design. Each module extends the base functionality of TiMBA in a specific way. TiMBA includes support for TiMBA Charts, an interactive dashboard for exploring simulation results.

Launch the dashboard with:

timba dashboard

This opens a local web application in your browser, providing interactive visualisations of the most important model outputs.

By default, the dashboard loads the two most recent simulation results from the standard TiMBA output directory. If no simulation has been executed yet, the dashboard cannot be started. Users can also specify alternative output directories or select a different number of scenarios for comparison.

To launch the dashboard automatically after a simulation finishes, use:

timba run -SD True

TiMBA also supports a dedicated Carbon Module:

timba carbon

This module estimates carbon stocks in forest biomass, forest soils, deadwood, litter, harvested wood products, and substitution effects based on TiMBA simulation results and historical statistics on forest resources and forest product markets (Honkomp 2026).

5. Project structure

After downloading the input data (timba load), the project directory is organised as follows:

data
└── input
    ├── 01_Input_Files
    │   └── scenario_input.xlsx          # Main scenario definition and model inputs
    ├── 02_Additional_Information
    │   ├── additional_information.xlsx  # Country, commodity and auxiliary information
    │   └── worldprice.xlsx              # Initial world price data
    └── 03_Serialization
        ├── AddInfoContent.pkl
        ├── WorldDataContent.pkl
        └── WorldPriceContent.pkl

The serialization files are generated automatically and contain cached representations of the processed input data. They are used to speed up subsequent model runs.

After simulation, TiMBA creates an output directory inside the data folder.

data
└── output
    └── data
        ├── DataContainer_<scenario><timestamp>.pkl
        ├── results_<timestamp>.csv
        ├── worldprices_<timestamp>.csv
        ├── forest_<timestamp>.csv
        ├── manufacture_<timestamp>.csv
        └── results_aggregated_<timestamp>.csv
    └── logs
        ├── TiMBA.log
        └── <scenario><timestamp>_info.yml

The output files contain:

File	Description
`TiMBA.log`	Log file containing information about the simulation process.
`<scenario><timestamp>_info.yml`	Records all model input parameters for reproducibility and transparency.
`DataContainer_<scenario><timestamp>.pkl`	Contains the complete serialized simulation output for efficient reuse.
`results_<timestamp>.csv`	Main simulation results in csv format.
`worldprices_<timestamp>.csv`	World price results in csv format.
`forest_<timestamp>.csv`	Forest resources-related results in csv format.
`manufacture_<timestamp>.csv`	Manufacturing sector-related results in csv format.
`results_aggregated_<timestamp>.csv`	Results aggregated at the continental level in csv format.

To ensure reproducibility, TiMBA never overwrites existing simulation results.

6. Model settings

Multiple settings are integrated for the timba run command to allow users to interact with the model and adapt the modelling parameters to their research interests. The following chapter provides a brief overview of the model settings. A detailed description of the settings is provided in the model documentation (TI-FSM 2025).

This guarantees that previous simulation results remain available for comparison and documentation.

Basic model settings include:

Option	Parameter	Type	Default
`-Y`, `--year`	`year`	int	default_year
`-MP`, `--max_period`	`max_period`	int	default_max_period
`-PP`, `--calc_product_price`	`calc_product_price`	str	default_calc_product_price
`-WP`, `--calc_world_price`	`calc_world_price`	str	default_calc_world_price
`-MB`, `--material_balance`	`material_balance`	str	default_MB
`-GMB`, `--global_material_balance`	`global_material_balance`	bool	global_material_balance
`-TF`, `--trans_imp_exp_factor`	`transportation_impexp_factor`	float	default_transportation_impexp_factor
`-S`, `--serialization`	`serialization`	bool	serialization_flag
`-D`, `--dynamization`	`dynamization_activated`	bool	dynamization_activated
`-COQ`, `--cleaned_opt_quantity`	`cleaned_opt_quantity`	bool	cleaned_opt_quantity
`-CP`, `--capped_prices`	`capped_prices`	bool	capped_prices
`-VO`, `--verb_opt_log`	`verbose_optimization_logger`	bool	verbose_optimization_logger
`-VT`, `--verb_calc_log`	`verbose_calculation_logger`	bool	verbose_calculation_logger
`-FP`, `--folderpath`	`folderpath`	Path	`cwd`
`-C`, `--activate_cmodule`	`activate_cmodule`	bool	False

Basic add-on module settings include (see add-on modules for TiMBA):

The activation of the carbon module [default: True]

Note:
TiMBA is delivered with a validated set of default settings that were tested for stability and consistency. These default parameters can be modified when executing the package via CLI or directly in default_parameters.py. Please note that any parameters specified in the CLI will overwrite those defined in default_parameters.py.
Not all combinations of functionalities and settings have been tested or validated. In particular, shadow and calculated price modes for country- and product-level (PP) and world (WP) prices must be applied consistently. Mixing the two (e.g., PP="calculated_PP" and WP="shadow_WP") is currently not supported and may result in an error.

6.1. Settings as parameters

The CLI provides access to basic model settings and their default values. Check if CLI command is registered and available on your computer by executing:

timba run --help

Default settings can be changed in the following way: (Note that the change of default settings as described below is for demonstration purposes only, and the results have not been validated.):

timba run -MP=5 -MB="RCG_specific" -CP="True"

For this example, TiMBA will simulate 5 periods using calculated prices as product prices and shadow prices as world market prices.

6.2. Advanced settings

In addition to the settings accessible via the CLI, users can control advanced settings through changes in Defines.py Advance settings include:

solver settings (like accuracy, number of iterations and penalties)
conversion factors

Caution: The model results were validated for a selection of setting combinations.
Some setting combinations might not be coherent and can lead to errors in the simulations or to unreliable results. Those combinations have neither been tested nor validated.
Note: TiMBA was tested with a pre-defined number of periods and respective periods' lenght. If the number of periods is changed, it is necessary to compare and adjust this in the ExogChange sheet in the input file.

7. TiMBA extended model description

TiMBA is a partial economic equilibrium model for the global forest products market. The market equilibrium is subject to market clearance and constraints balancing necessary raw materials and produced wood products and limiting the trade (Samuelson 1952). The model structure distinguishes between raw, intermediate and end products. TiMBA differentiates three types of roundwood (wood fuel, coniferous and non-coniferous industrial roundwood), two additional raw products for paper production (other fibre pulp and waste paper), two intermediate products (mechanical and chemical pulp) and eight finished products (coniferous and non-coniferous sawnwoods, veneer sheets and plywood, particle board, fibreboard, newsprint, printing and writing paper, and other paper and paperboards). Except for sawnwoods, intermediate and end products are produced from a mix of coniferous and non-coniferous industrial roundwood. Scenario simulations with TiMBA are guided by parameters and assumptions shaping future developments. In the model framework, wood products are implicitly treated as perfect substitutes, regardless of their origin, as long as they belong to the same commodity group. As the optimization of the market equilibrium in a given year does not include an elasticity of substitution, demand is merely shifted by changes in income and price (Murray et al. 2004). The supply of roundwood depends on wood prices and forest development which in turn is basically determined by the growth dynamics of forest stock, the change in forest area, and harvest volumes. The GDP development indicating national incomes is an important driver of change. In TiMBA, demand for wood-based products is positively correlated to income, thus, an increase in income basically leads to an increase in demand. Forest area development and thus, timber supply is coupled to GDP per capita developments based on the concept of the environmental Kuznets curve (Panayotou 2004). In its basic version, TiMBA uses the assumptions made in the “Middle of the road” scenario described in “The Shared Socioeconomic Pathways” (the so called SSP2 scenario) to model future GDP developments and population growth. This scenario describes a world of modest population growth and where social, economic and technological trends continue similarly to historical patterns (Riahi et al. 2017). Price and income elasticities of demand are taken from Morland et al. (2018). Further exogenous specifications on technology developments (input-output coefficients and manufacturing cost) are estimated based historical developments from 1993-2020. Information on trade inertia are based on WTO data as provided in the GFPM (Buongiorno et al. 2015; GFPM version 1-29-2017-World500) while data on WTO Ad-valorem taxes rates were recalculated by the authors as described in Schier et al. (2026). The base year for the scenario simulations with the current version of TiMBA is 2020. The input data used for simulation with TiMBA needs to be calibrated and provided in a source file prior to model runs. This file is provided together with the model (scenario_input.xlsx) as described in 3. Use TiMBA. The model input data calibration procedure is described in Buongiorno and Zhu (2015) and altered according to Schier et al. (2018). The input data for calibrating the model are obtained from three global databases: The FAO forestry statistics (FAOSTAT), the FAO Forest Global Resources Assessment (FAO 2020) and the World Bank Development Indicators (World Bank). The model output comprises information about production, consumption and trade quantities, and prices as well as forest development. The model concept bases on the formal description of the Global Forest Products Model (GFPM) (Buongiorno et al. 2015, Buongiorno et al. 2003).

8. Supplementary modules for TiMBA

TiMBA is designed as a modular modelling framework that can be extended with additional applications to enable further functionalities. Depending on the use case and the research question, users can activate or deactivate these modular extensions via the CLI or through the default_parameters. In this way, the computational load is tailored to the user's needs. The modules are designed as separate packages which can be imported into the main TiMBA application. These packages are usually hosted on the TI-FSM GitHub or PyPI pages. While full compatibility of extensions with each other is targeted, certain combinations of functionalities may cause errors or lead to longer calculation times. Such issues can be reported in the respective repositories.

This sections provides an overview of available extensions. Beyond the activation, the extensions offer a range of settings to adapt their functionality to specific use cases. For details about each module and its configuration options, users should refer the respective GitHub repositories.

Module	Description	Activation	GitHub project	Citation
Carbon Module	tracks carbon stocks and stock changes across pools in the forestry sector including aboveground and belowground biomass, forest soils, deadwood and litter, and harvested wood products. The module applies updated guidelines of the IPCC (2019). A visualization dashbord for carbon results is generated automatically.	`activate_cmodule=True` in `default_parameters.py` or `-C=True` in the CLI	C-Module	Honkomp (2025)
TiMBA Charts	an analysis toolbox with multiple dashboards about the main TiMBA results	`timba run -SD True` to open the toolbox directly after simulation has ended, or `timba dashboard` to open the toolbox indepently from a specific TiMBA run	TiMBA Charts	Morland, C., Tandetzki, J., & Honkomp, T. (2026)

9. Roadmap and project status

The development of TiMBA is ongoing and we are already working on future releases.

To provide an easy way to visualize and analyse the output of TiMBA we published the interactive analysis toolbox TiMBA_Charts (Morland et al. 2025) that is also available in our TiMBA repository.

Several projects are currently extending different components of TiMBA:

In the project iNFORSu, we are working on the revision of the module computing forest area and stock development. Forest area development should not longer be only depend on the of GDPpc. Instead, further drivers significantly shaping forest area could be included into the simulation. In addition, the authors are working on the utilization of Global Vegetation Models (GVM) results on forest area development and net-primary production for integration into TiMBA simulations. This should bring forest development and thus, wood supply closer to reality.
The high flexibility of TiMBA allows it to cover a large panel of policy designs and conduct sensitivity analyses. In the project BioSDG and CarbonLeak, we extended the model to track and quantify carbon fluxes and stocks related to the forest sectors. Based on IPCC-based methods, carbon stocks in forest biomass and harvested wood products as well as substitution effects are quantified for each simulation period. This extension enables in-depth impact assessments of forest-based climate mitigation policies (e.g., carbon pricing policies and mitigation target-setting policies for the Land Use, Land Use Change and Forestry sector).
In another project, we are implementing bilateral trade flows into the model framework. This step is important to enhance policy impact assessments on e.g., leakage effects.
Given the fast processing time, we are extending TiMBA to conduct exhaustive uncertainty analysis using Monte Carlo simulations. While these Monte Carlo simulations are currently used in the quantification of carbon stocks, their applications can be deployed to any input data of the model.

Frequently check TiMBA repository for new releases.

10. FAIR research software

We attempt to adhere as much as possible to the FAIR Principles for research software (e.g. see Barker et al. 2022 and Chue Hong et al. 2022) and reach high standards of scientific quality and openness. If you find that TiMBA or its documentation could be improved, we would greatly appreciate your feedback. Please submit it as an Issue in the GitHub repository or via email to wf-timba@thuenen.de.

In the following we detail our efforts and considerations.

10.1 Findable

Zenodo provides a DOI with extensive metadata according to the DataCite schema.
- Separate DOIs for each release and one representing all releases.
A set of SWHID (Software Hash IDentifiers) is created by the Software Heritage archive for each release and refereneces the code in a very granular fashion.
An accompanying software paper makes sure the software is also referenced in search indices that focus on journal articles.
We provide rich metadata also as linked data in the form of the file codemeta.json in the CodeMeta standard.
We provide machine-actionable citation information in the Citation File Format (CFF).

10.2 Accessible

The source code can be accessed in the form of a git remote from GitHub, via http from Zenodo, but also by Python package managers through the Python Package Index, both as source distribution and as a Wheel.
Metadata is preserved to a very high degree of safety in a redundant fashion. The DataCite type metadata not only preserved by DataCite but also by Zenodo and in the form of a file zenodo.json on GitHub and in the SoftwareHeritage datacenters. These three repositories also hold the file codemeta.json.
We recognize that GitHub, a commercial platform owned by Microsoft, is subject to business and strategic considerations that may raise concerns regarding data governance, security, adherence to scientific community standards, and long-term sustainability. Nevertheless, GitHub's seamless integration with Zenodo provides substantial benefits, including automated archiving and DOI assignment, which would be difficult to achieve with our current resources. We hope that a comparable integration will be developed in the future for better suited Forges such as Forgejo or self-hosted GitLab.

10.3 Interoperable

10.3.1 Software interoperability

TiMBA installs both as executable script (timba_run) as well as as a Python module (TiMBA). The script copies its output also to STDOUT and allows to chain TiMBA with other command line tools in a classical UNIX fashion. Importing the module allows users to integrate TiMBA into their own Python programs.
We also maintain extensions to TiMBA (Carbon Module and TiMBA Charts) in the form of Python packages that can be used by importing them. These packages also adhere to FAIR principles, are version-controlled in GitHub and can be referenced with DOIs registered by Zenodo.

10.3.2 Data interoperability

TiMBA relies on a set of required input data. The canonical versions of these data (a non-trivial scientific output) are also kept version controlled in GitHub and releases are published to Zenodo.
The main input data file is Office Open XML ("Microsoft Excel") format. We are aware that this format is not a good choice from a purely technical point of view and has severe shortcomings with regard to robustness, stability over time, machine readability, interoperability, platform independence and accessibility. However, this is the optimal format to make the input understandable and modifyable by our main target group, who understands Microsoft Excel very well but might struggle with less common formats and conventions. This is a cultural and educational problem we can't solve in this context. We are familiar with and track projects such as Frictionnless Data with its Data Package standard. We hope to eventually be able to combine user-friendliness with robust data standards to represent our input data.
TiMBA output is written to universally readable CSV-files, while a serialized PKL file preserves the complete simulation state for efficient reuse in Python. We plan to describe these files with standardized metadata in the future.

10.4 Reusable

TiMBA has dependencies and sub-dependencies which are extensively recorded in the uv.lock file to ensure reproducibility.
We continually strife to keep and improve software quality. We utilize automated CI processes (testing for different platforms, linting) in the form of GitHub workflows.
We have licensed the software under the GNU Affero General Public License. On the one hand side this license is most compatible with the potential need to In-license further dependencies. On the other hand, as a Copyleft license, it is very well suited to support Open Science, reproducibility and transparency (von Waldow, 2024).
The scientific background and in particular prior software on which this work improves, is extensively documented and referenced in this README file.
Python as a programming language, adherence to an improved (ruff-defaults) PEP8 style guide, user-friendly formatting of input data, and instructions about how to contribute increase the chances that this software is being reused in our research community.

11. Contributing to the project

We welcome contributions, additions and suggestion to further develop or improve the code and the model. To check, discuss and include them into this project, we would like you to share your ideas with us so that we can agree on the requirements needed for accepting your contribution.

You can contact us directly via GitHub by creating issues, or by writing an Email to:

wf-timba@thuenen.de

So far, this README serves as a comprehensive introduction and guidance on how to get started. ´The model documentation (TI-FSM 2025) and model validation (TI-FSM 2026) enables a deeper dive.

12. Authors

TiMBA was developed and written by an authors' collective named Thünen Institute Forest Sector Modelling (TI-FSM).

The individual authors are listed in alphabetical order

13. Contribution statement

Within the authors' collective TI-FSM, the authors have contributed over years their individual strengths and knowledge to make the model work:

Author	Conceptualization and theoretical framework	Methodology	Data Curation and Management	Formal Analysis	Programming	Writing and Documentation	Visualization	Review and Editing	Supervision
Christian Morland	X	X	X	X	X	X	X	X
Franziska Schier	X	X	X	X		X		X	X
Julia Tandetzki	X	X	X	X	X	X	X	X
Tomke Honkomp	X	X	X	X	X	X	X	X

14. License and copyright note

Licensed under the GNU AGPL, Version 3.0.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/agpl-3.0.txt.

15. Acknowledgements

This work is the result of great joint efforts of the forest products market analysis team at the Thünen Institute of Forestry and others from 2018 to 2024. In the last years, many people made important contributions to this work. Without their support, reflection, and constructive criticism, this undertaking would not have been as successful as it turns out to be now. We would like express our gratitude to all of them. In particular, we would like to thank

Pixida GmbH and especially Tobias Hierlmeier for professional support in revising and restructuring the model architecture and code and being valuable help in programming tasks
Thünen Institute Service Centre for Research Data Management and especially Harald von Waldow for providing expertise, consultation, and support during the release process
Holger Weimar and Matthias Dieter for the trustful and cooperative working environment, valuable support and critical discussion and the opportunity to keep on going
Johanna Schliemann and Gregor Müller for technical support whenever needed
The Thünen Institut of Forestry and its Head Matthias Dieter for providing financial resources over the years
makeareadme.com for providing the template this README is leaned on.

16. References

Barker, M., Chue Hong, N.P., Katz, D.S. et al. Introducing the FAIR Principles for research software. Sci Data 9, 622 (2022). https://doi.org/10.1038/s41597-022-01710-x
Buongiorno, J.; Zhu, S.; Zhang, D.; Turner, J.; Tomberlin, D. The Global Forest Products Model; Academic Press: Cambridge, MA, USA, 2003; ISBN 978-0-12-141362-0
Buongiorno, J. Global modelling to predict timber production and prices: The GFPM approach. Forestry 2015, 88, 291–303.
Buongiorno, J.; and Zhu, S. 2015. Technical change in forest sector models: The GFPM approach. Scand. J. For. Research, 30, 30-48.
GFPM - Global Forest Product Model is available at https://onedrive.live.com/?authkey=%21AEF7RY7oAPlrDPk&id=93BC28B749A1DFB6%21118&cid=93BC28B749A1DFB6
Chue Hong, N.P. et al. (2022) “FAIR Principles for Research Software (FAIR4RS Principles)”. Zenodo. Available at: https://doi.org/10.15497/RDA00068.
FAO. Global Forest Resources Assessment: Terms and Definitions; Forest Resources Assessment Working Paper 188; FAO: Rome, Italia, 2020; Available online: http://www.fao.org/3/I8661EN/i8661en.pdf
FAO. Global Forest Resources Assessment. 2022. Available online: https://fra-data.fao.org/
FAOSTAT. Forestry Production and Trade: Datenbank. Available online: https://www.fao.org/faostat/en/#data/FO
Morland, C.; Schier, F.; Janzen, N.; Weimar, H. Supply and demand functions for global wood markets: Specification and plausibility testing of econometric models within the global forest sector. For. Policy Econ. 2018, 92, 92–105
Morland, C.; Tandetzki, J.; & Honkomp, T. (2025). TiMBA Charts (v0.2.0). Zenodo. https://doi.org/10.5281/zenodo.15689299
Murray, B.C.; McCarl, B.A.; Lee, H.-C. Estimating Leakage from Forest Carbon Sequestration Programs. Land Econ. 2004, 80, 109–124
Panayotou, T. Empirical Tests and Policy Analysis of Environmental Degradation at Different Stages of Economic Development; Working Paper No. 238; International Labour Organization: Geneva, Switzerland, 1993; Available online: http://www.ilo.org/public/libdoc/ilo/1993/93B09_31_engl.pdf
Riahi, K.; van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’Neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O.; et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang. 2017, 42, 153–168.
Samuelson, Paul A. Spatial Price Equilibrium and Linear Programming; The American Economic Review, 1952, 42 (3), 283–303; Available online http://www.jstor.org/stable/1810381.
Schier, F.; Morland, C.; Tandetzki, J.; Honkomp, T. (2026). TI-Forest-Sector-Modelling/TiMBA_Additional_Information: Reworking data files and licenses (Version v1.0.2). Zenodo. https://doi.org/10.5281/zenodo.19466845
TI-FSM (2025) TiMBA - Timber market Model for policy-Based Analysis: Documentation of model structure, data, and parameters. Braunschweig: Johann Heinrich von Thünen-Institut, 35 p, Thünen Working Paper 263, DOI:10.3220/253-2025-16
von Waldow, H. (2024). Research Software Licensing Guide (Version v1.0.2). Zenodo. https://doi.org/10.5281/zenodo.14008091
TI-FSM (2026) TiMBA - Timber market Model for policy-Based Analysis: Validation of a partial equilibrium model. Braunschweig: Johann Heinrich von Thünen-Institut, 36 p, Thünen Working Paper 282, DOI:10.3220/253-2026-29
World Bank. World Development Indicators|DataBank. Available online: https://databank.worldbank.org/source/world-development-indicators

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.github		.github
TiMBA		TiMBA
documentation		documentation
images		images
paper		paper
test		test
.coveragerc		.coveragerc
.gitignore		.gitignore
.gitlab-ci.yml		.gitlab-ci.yml
.python-version		.python-version
.zenodo.json		.zenodo.json
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CONTRIBUTING.md		CONTRIBUTING.md
COPYING		COPYING
COPYRIGHT		COPYRIGHT
MANIFEST.in		MANIFEST.in
README.md		README.md
citation.bib		citation.bib
codemeta.json		codemeta.json
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
timba_logo_v3.png		timba_logo_v3.png
uv.lock		uv.lock

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