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Valuing Protected Area Tourism Ecosystem Services Using Big Data
Economic value from protected areas informs decisions for biodiversity conservation and visitor benefits. Calculating these benefits assists governments to allocate limited budget resources. This study estimated tourism ecosystem service expenditure values for a regional protected area network in So...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672632/ https://www.ncbi.nlm.nih.gov/pubmed/36396859 http://dx.doi.org/10.1007/s00267-022-01746-0 |
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author | Loch, Adam Scholz, Glen Auricht, Christopher Sexton, Stuart O’Connor, Patrick Imgraben, Sarah |
author_facet | Loch, Adam Scholz, Glen Auricht, Christopher Sexton, Stuart O’Connor, Patrick Imgraben, Sarah |
author_sort | Loch, Adam |
collection | PubMed |
description | Economic value from protected areas informs decisions for biodiversity conservation and visitor benefits. Calculating these benefits assists governments to allocate limited budget resources. This study estimated tourism ecosystem service expenditure values for a regional protected area network in South Australia (57 parks) using direct transactional data, travel costs and economic multipliers. The big dataset came from a comprehensive booking system, which helped overcome common limitations associated with survey data (e.g., key areas rather than full network and high zero-value observations). Protected areas returned AU$373.8 million in the 2018–19 base year to the South Australian economy. The results indicate that combined estimation methods coupled to big data sets provide information on baseline expenditure to engage with critical conservation and tourism sites (e.g., Kangaroo Island). In this case they offer a unique full area network expenditure estimate which is an improvement on typical survey approaches, highlighting the advantage of protected area managers investing in big data. Finally, as South Australian protected areas exceed that in many other contexts the study offers important inputs to funding narratives and protected area expansion in line with global assessment targets. |
format | Online Article Text |
id | pubmed-9672632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-96726322022-11-18 Valuing Protected Area Tourism Ecosystem Services Using Big Data Loch, Adam Scholz, Glen Auricht, Christopher Sexton, Stuart O’Connor, Patrick Imgraben, Sarah Environ Manage Article Economic value from protected areas informs decisions for biodiversity conservation and visitor benefits. Calculating these benefits assists governments to allocate limited budget resources. This study estimated tourism ecosystem service expenditure values for a regional protected area network in South Australia (57 parks) using direct transactional data, travel costs and economic multipliers. The big dataset came from a comprehensive booking system, which helped overcome common limitations associated with survey data (e.g., key areas rather than full network and high zero-value observations). Protected areas returned AU$373.8 million in the 2018–19 base year to the South Australian economy. The results indicate that combined estimation methods coupled to big data sets provide information on baseline expenditure to engage with critical conservation and tourism sites (e.g., Kangaroo Island). In this case they offer a unique full area network expenditure estimate which is an improvement on typical survey approaches, highlighting the advantage of protected area managers investing in big data. Finally, as South Australian protected areas exceed that in many other contexts the study offers important inputs to funding narratives and protected area expansion in line with global assessment targets. Springer US 2022-11-18 2023 /pmc/articles/PMC9672632/ /pubmed/36396859 http://dx.doi.org/10.1007/s00267-022-01746-0 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Loch, Adam Scholz, Glen Auricht, Christopher Sexton, Stuart O’Connor, Patrick Imgraben, Sarah Valuing Protected Area Tourism Ecosystem Services Using Big Data |
title | Valuing Protected Area Tourism Ecosystem Services Using Big Data |
title_full | Valuing Protected Area Tourism Ecosystem Services Using Big Data |
title_fullStr | Valuing Protected Area Tourism Ecosystem Services Using Big Data |
title_full_unstemmed | Valuing Protected Area Tourism Ecosystem Services Using Big Data |
title_short | Valuing Protected Area Tourism Ecosystem Services Using Big Data |
title_sort | valuing protected area tourism ecosystem services using big data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672632/ https://www.ncbi.nlm.nih.gov/pubmed/36396859 http://dx.doi.org/10.1007/s00267-022-01746-0 |
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