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Explaining landscape preference heterogeneity using machine learning-based survey analysis

2021; Taylor & Francis; Linguagem: Inglês

10.1080/01426397.2020.1867713

1469-9710

Xiaozi Liu, Endre Tvinnereim, Kristine Grimsrud, Henrik Lindhjem, Liv Guri Velle, Heidi Iren Saure, Hanna Lee,

Economic and Environmental Valuation

We conducted a national survey on a high-quality internet panel to study landscape preferences in Norway, using photos as stimuli. We examined preference heterogeneity with respect to socio-demographic characteristics and latent topics brought up by the respondents, using ordinal logistic regression and structural topic modelling (STM), a machine learning-based analysis. We found that pasture landscapes are the most favoured (55%), while densely planted spruce forests are the least favoured (8%). The contrast was particularly strong between eastern and western Norway, between men and women, and between young and old. STM revealed that the choices were mainly driven by the preference for landscape openness, especially by women. Other important drivers were concerns regarding reforestation of former farmlands, aesthetic properties, forest management, biodiversity issues, and cultural values. Our results suggest that landscape policies may clash with socio-cultural preferences, and failure to account for these may undermine the success of a policy.