Water resource economics, natural resource economics, environmental economics, ecological economic modeling, ecological economics, environmental policy, development economics.
Topics:
Behavioural aspects of environmental problems: Use of evolutionary approaches to investigate how people react to environmental policy.
Resource scarcity
Climate-economic Models: Understand how economic agents think and decide about climate change and climate protection
Sustainable Development
Methods:
Agent-Based-Modeling
Genetic algorithms
Evolutionary economics
Behavioural economics
Ecological economics
Complexity Theory
Environmental Economics, Resource Economics, Behaviour Economics, Social Security/ Health Economics, Sustainability, Development Economics
Agent based modeling, Environmental economics, Risk analysis
Agent based modelling in water management, especially focused in extreme phenomena such floods and droughts.
I am an environmental economist at UFZ - Helmholtz Centre for Environmental Research in Leipzig, Germany. I did my PhD (Dr. rer. pol.) in environmental economics at the Martin Luther University Halle-Wittenberg in 2017. Before that, I received my master’s (2013; economics) and bachelor’s degrees (2010; cultural studies) from the same university.
My research focus is on the economic analysis of agri-environmental policy instruments as means to navigate ecosystem service trade-offs in multifunctional landscapes. In this context, I am particularly interested in identifying policy instruments and instrument mixes allowing to align societal preferences with biophysical potential of landscapes to provide multiple ecosystem services. Here, the mutual relationship between regulatory and incentive-based instruments is of much interest. Using agent-based modelling, but also more qualitative approaches, I look at the emerging landscape-level patterns that result from various policy mixes given realistic descriptions of farmers’ behaviour and institutional settings.
I am investigating the use of machine learning techniques in non-stationary modeling environments to better reproduce aspects of human learning and decision-making in human-natural system simulations.