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Using the Multidimensional AIMES to Estimate Connection-to-Nature in an Australian Population: A Latent Class Approach to Segmentation

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posted on 2023-03-14, 00:26 authored by Bradley JorgensenBradley Jorgensen, J Meis-Harris
Individuals can interact and develop multiple connections to nature (CN) which have different meanings and reflect different beliefs, emotions, and values. Human population are not homogenous groups and often generalised approaches are not effective in increasing connectedness to nature. Instead, target-group specific approaches focusing on different segments of the population can offer a promising approach for engaging the public in pro-environmental behaviours. This research employed latent class analysis to identify subgroups of individuals in a large, representative sample (n = 3090) of an Australian region. Three groups were identified using the AIMES measure of CN with its focus on five types of connection to nature. The high CN group comprised about one-third (35.4%) of participants while the group with the lowest profile of scores contained around a fifth (18.6%) of participants. The majority (46.0%) of participants registered CN levels between the high and low groups. These classes were then regressed on predictor variables to further understand differences between the groups. The largest, consistent predictors of class membership were biocentric and social-altruistic value orientations, stronger intentions to perform pro-environmental behaviours in public (e.g., travel on public transport), the amount of time spent in nature, and the age of participants.

Funding

This research was funded by the Department of Environment, Land, Water, and Planning, grant number Pure ID 249460410.

History

Publication Date

2022-10-01

Journal

International Journal of Environmental Research and Public Health

Volume

19

Issue

19

Article Number

12307

Pagination

13p.

Publisher

MDPI

ISSN

1660-4601

Rights Statement

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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