Environmental Valuation with Discrete Choice Experiments in R

Petr Mariel is a professor at the Department of Quantitative Methods of the University of the Basque Country UPV/EHU. His research focus is centred on discrete choice modelling applied mainly to environmental valuation, but he has also worked in the field of health, public and urban economics.Danny Campbell is a professor at the University of Stirling Business School's Economics Division and specialises in stated and revealed preference primarily within environmental economics but also in the domains of public health services, analysing food choices, and valuing cultural heritage.Erlend Dancke Sandorf is an Associate Professor at the School of Economics and Business at the Norwegian University of Life Sciences. His research focuses on the application of choice modelling to revealed and stated preference data to understand the values that people have for the environment ranging from investments in sustainable technology, to recreation, and conservation efforts.Jürgen Meyerhoff is a Senior Researcher at the Berlin Centre for Empirical Economics (BCEE) at HWR Berlin. His research focuses on methodological aspects of discrete choice experiments, particularly when employed to record stated preferences, investigating how the design of DCE influences choices.Ainhoa Vega-Bayo is an Associate Professor at the Department of Economic Analysis of the University of the Basque Country UPV/EHU. Her research focuses on program evaluation methods and discrete choice models applied mainly to education, labour and economic development.Rebecca Blevins is a PhD student at the Basque Centre for Climate Change (BC3). Her research focuses on the incorporation of subjective well-being indicators in integrated assessment models to evaluate the distributional impacts of climate and just transition policies.

• Chapter 1. Introduction.- Chapter 2. Steps of a Discrete Choice Experiment.- Chapter 3. Random utility models: Theoretical background.- Chapter 4. Case study.- Chapter 5. Experimental design.- Chapter 6. Data collection in Shiny.- Chapter 7. From Raw Data to Insights.- Chapter 8. Maximum Likelihood and Related Issues.- Chapter 9. Estimation.- Chapter 10. Post-estimation analysis.- Chapter 11. Final thoughts.