Modelling welfare estimates in discrete choice experiments for seaweed-based renewable energy

• DOI: 10.1371/journal.pone.0260352
• Publication Date: 2021-11-29T18:25:58Z
• Authors: Petr Mariel; Simona Demel; Alberto Longo
• Keywords: /dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; 330; Science; Q; R; 0211 other engineering and technologies; name=SDG 7 - Affordable and Clean Energy; 02 engineering and technology; Seaweed; Models, Biological; 7. Clean energy; /dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy; Biofuels; Medicine; Renewable Energy; Algorithms; Research Article

We explore what researchers can gain or lose by using three widely used models for the analysis of discrete choice experiment data—the random parameter logit (RPL) with correlated parameters, the RPL with uncorrelated parameters and the hybrid choice model. Specifically, we analyze three data sets focused on measuring preferences to support a renewable energy programme to grow seaweed for biogas production. In spite of the fact that all three models can converge to very similar median WTP values, they cannot be used indistinguishably. Each model is based on different assumptions, which should be tested before their use. The fact that standard sample sizes usually applied in environmental valuation are generally unable to capture the outcome differences between the models cannot be used as a justification for their indistinct application.