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dc.contributor.authorLiu, Xiaozi
dc.contributor.authorTvinnereim, Endre
dc.contributor.authorGrimsrud, Kristine
dc.contributor.authorLindhjem, Henrik
dc.contributor.authorVelle, Liv Guri
dc.contributor.authorSaure, Heidi Iren
dc.contributor.authorLee, Hanna
dc.date.accessioned2022-02-11T08:23:32Z
dc.date.available2022-02-11T08:23:32Z
dc.date.created2020-09-28T14:06:36Z
dc.date.issued2021
dc.identifier.citationLandscape Research. 2021, .en_US
dc.identifier.issn0142-6397
dc.identifier.urihttps://hdl.handle.net/11250/2978381
dc.description.abstractWe 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.en_US
dc.language.isoengen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleExplaining landscape preference heterogeneity using machine learning-based survey analysisen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber18en_US
dc.source.journalLandscape Researchen_US
dc.identifier.doi10.1080/01426397.2020.1867713
dc.identifier.cristin1834304
dc.relation.projectNorges forskningsråd: 268243en_US
dc.relation.projectNorges forskningsråd: 280393en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal