DCEs are widely used to understand the preferences of individuals and farmers regarding policies and measures related to land use, biodiversity conservation, and climate change adaptation. However, many of these decisions have an important spatial dimension: preferences may vary depending on the geographic context (e.g., exposure to extreme events, surrounding land use, region, etc.).

The aim of this thesis is to conduct a state-of-the-art review of how spatial information (geographic variables, GIS data, maps, spatial heterogeneity, etc.) is incorporated into DCEs, especially in applications related to agriculture, land use, and climate change adaptation. The task will consist of identifying and systematizing peer-reviewed literature that combines DCEs and spatial information, classifying methodological approaches, and synthesizing best practices and research gaps.

Requirements (desirable):
• Interest in stated preference valuation and in DCE/choice modelling.
• Interest in spatial information and how to integrate it into decision analysis.
• Ability to read and synthesize scientific articles.

Further reading:
Stetter, C., & Sauer, J. (2024). Tackling climate change: Agroforestry adoption in the face of regional weather extremes. Ecological Economics, 224, 108266. https://doi.org/10.1016/j.ecolecon.2024.108266

Stetter, C., & Cronauer, C. (2025). Climate and soil conditions shape farmers’ climate change adaptation preferences. Agricultural Economics, 56(2), 165-187. https://doi.org/10.1111/agec.12870

Badura, T., & Schaafsma, M. (2026). Incorporating spatial complexity and variability into the design of stated choice experiments for biodiversity policy support. Resource and Energy Economics, 101565. https://doi.org/10.1016/j.reseneeco.2026.101565

For more information, contact pacaytot@b-tu.de.

A recent investigation into fraudulent uses of EU Common Agricultural Policy (CAP)  funds uncovered damages amounting to roughly 4.65 billion euros, over 10% of the annual volume of CAP direct payments to private landholders. This project will employ a contract theory framework to review and analyse empirical instances of corruption, fraud, and strategic noncompliance in agri-environmental schemes and payments for ecosystem services more broadly, with the aim of identifying common typologies of corrupt behaviour across scheme designs and governance contexts. The project will conclude by mapping gaps in the literature and proposing directions for future research on the design of corruption-resistant conservation payment instruments.

For more information and application contact christopher.stapenhurst(at)b-tu.de.

Further reading:
1. European Court of Auditors (2022). Special Report 14/2022: The Commission's response to fraud in Common Agricultural Policy spending. Luxembourg: ECA. https://www.eca.europa.eu/en/publications?did=61337
2. Luca Tacconi, David Aled Williams. (2020). Corruption and Anti-Corruption in Environmental and Resource Management.Annual Review Environment and Resources. 45:305-329. https://doi.org/10.1146/annurev-environ-012320-083949
3. Aidt, T.S. (2003). Economic analysis of corruption: a survey. The Economic Journal, 113(491),F632–652.https://doi.org/10.1046/j.0013-0133.2003.00171.
4. Engel S (2016), The Devil in the Detail: A Practical Guide on Designing Payments for Environmental Services. International Review of Environmental and Resource Economics, Vol. 9 No. 1-2 pp. 131–177, doi: https://doi.org/10.1561/101.00000076
5. CSD (2019). Fraudulent Use of EU Funds in the Field of Agriculture. Sofia: CSD.https://csd.eu/fileadmin/user_upload/publications_library/files/2019_01/BRIEF_82_ENG_FINAL_WEB.pdf

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Brun et al. (2022) have published CHELSA-BIOCLIM+, which are climate data, that can be used to determine the living conditions of species. The goal of this project is to analyse a similar data set, which includes common climate scenarios.

The main task will be to: visualise the bioclimatic variables and compare them between climate scenarios; perform statistical analysis by calculating trends and variability; analyse spatially where the impact of climate change on biodiversity is the largest across Europe. 

Requirements: Programming experience is helpful but not necessary.

Further reading:
Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., Karger, D. N. (2022). CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution. EnviDat. https://www.doi.org/10.16904/envidat.332

Dependence of Europe's most threatened mammals on movement (2024). Spatial Adaptive Trajectories (SATs) for 39 threatened mammal species to see if they can migrate fast enough. https://doi.org/10.1111/cobi.14315

Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. In Climate Change 2021: The Physical Science Basis. doi:10.1017/9781009157896.006 https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-4/

For more information, contact nils.walper(at)b-tu.de.

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Climate change is expected to increase land use conflicts as we need land to produce not only food, but increasingly also renewable energy. Different Integrated Assessment Models (IAMs) assess future changes to land use for different scenarios such as sustainable societies, scenarios of regional rivalry and intensive use of fossil fules. To determine how much land will be allocated to different land uses (e.g. food production, energy production), IAMs use "Computable General Equilibrium (CGE) Models". The purpose of this thesis is to describe the CGE models used in 5 common IAMs, highlighting their model structure and assumptions. 

Requirements: Interest in economics (relevant modules offered by the Chair of Environmental Economics), interest in modelling, in-depth reading of a few pre-defined scientific articles.

Further Reading:

Govind, G., Hertel, T.W., Baldos, U.L.C., Liu, J. (2022). Land Use in Computable General Equilibrium Models. Journal of Global Economic Analysis. https://doi.org/10.21642/JGEA.070103AF

Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S. & Lutz, W. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions trajectories: A survey. Global Environmental Change. https://doi.org/10.1016/j.gloenvcha.2016.05.009

Zhao, X., Calvin, K. V., Wise, M. A., Patel, P. L., Edmonds, J. A., & Kyle, G. P. (2020). The critical role of conversion cost and comparative advantage in modeling agricultural land use change. Climate Change Economics. https://doi.org/10.1142/S201000782050001X

For more information, contact nils.walper(at)b-tu.de.

There are many estimates of the economic costs of wolves, but so far no estimates of the economic benefits of wolves in Germany. For example, wolves can attract tourists, support natural forest regeneration, and may even reduce the number of road accidents. Over the last two decades, wolves have gradually renaturalised in East Germany, making it an excellent case study for measuring these potential benefits. 

The project will involve finding suitable data and applying the appropriate econometric techniques. Familiarity with regression techniques and software is ideal.

For more information and application contact christopher.stapenhurst(at)b-tu.de.

Further Reading:
Erlend B Nilsen et al. (2007) Wolf reintroduction to Scotland: public attitudes and consequences for red deer management. Proc Biol Sci 1 April; 274 (1612): 995–1003. https://doi.org/10.1098/rspb.2006.0369
Lee, Y., Harrison, J.L., Eisenberg, C. et al. (2012) Modeling biodiversity benefits and external costs from a keystone predator reintroduction policy. J. Mt. Sci. 9, 385–394. https://doi.org/10.1007/s11629-009-2246-1
Estifanos, T. et al. (2018). Protection of the Ethiopian Wolf: What are tourists willing to pay for? Unknown.https://doi.org/10.22004/AG.ECON.272805

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Stigler (1945) uses a simple linear program to calculate the cheapest way to meet an average adult's nutritional needs using US 1939 prices.
The goal of this project is to use the same technique to estimate the best choice of crops of meeting Germany's nutritional needs whilst minimising biodiversity-loss.

The main tasks will be to: obtain modern nutritional recommendations; obtain a database of nutritional content of available crops; obtain a database of land requirements for each crop, and then use the simplex algorithm to calculate the optimal crop mix. Programming experience is helpful but not strictly necessary.

Further reading:
Walter Willett et al. (2019) "Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems".  The Lancet, Volume 393, Issue 10170, 447 - 492. https://doi.org/10.1016/S0140-6736(18)31788-4
Jessica A. Gephart, et al. (2016) "The environmental cost of subsistence: Optimizing diets to minimize footprints". Science of The Total Environment 553,  Pages 120-127. https://doi.org/10.1016/j.scitotenv.2016.02.050
Rozenn Gazan, Chloé M C Brouzes, Florent Vieux, Matthieu Maillot, Anne Lluch, Nicole Darmon, (2018) "Mathematical Optimization to Explore Tomorrow's Sustainable Diets: A Narrative Review". Advances in Nutrition 9:5, 602-616. https://doi.org/10.1093/advances/nmy049.
George J. Stigler (1945) "The Cost of Subsistence".  American Journal of Agricultural Economics, Volume 27, Issue 2, Pages 303–314, https://doi.org/10.2307/1231810

For more information, contact christopher.stapenhurst(at)b-tu.de.