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Human dependence on natural resources in rapidly urbanising South African regions
Stockholm University, Faculty of Science, Stockholm Resilience Centre. Council for Scientific and Industrial Research, South Africa.
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Number of Authors: 72019 (English)In: Environmental Research Letters, ISSN 1748-9326, E-ISSN 1748-9326, Vol. 14, no 4, article id 044008Article in journal (Refereed) Published
Abstract [en]

Enhancing the governance of social-ecological systems for more equitable and sustainable development is hindered by inadequate knowledge about how different social groups and communities rely on natural resources. We used openly accessible national survey data to develop a metric of overall dependence on natural resources. These data contain information about households' sources of water, energy, building materials and food. We used these data in combination with Bayesian learning to model observed patterns of dependence using demographic variables that included: gender of household head, household size, income, house ownership, formality status of settlement, population density, and in-migration rate to the area. We show that a small number of factors-in particular population density and informality of settlements-can explain a significant amount of the observed variation with regards to the use of natural resources. Subsequently, we test the validity of these predictions using alternative, open access data in the eThekwini and Cape Town metropolitan areas of South Africa. We discuss the advantages of using a selection of predictors which could be supplied through remotely sensed and open access data, in terms of opportunities and challenges to produce meaningful results in data-poor areas. With data availability being a common limiting factor in modelling and monitoring exercises, access to inexpensive, up-to-date and free to use data can significantly improve how we monitor progress towards sustainability targets. A small selection of openly accessible demographic variables can predict household's dependence on local natural resources.

Place, publisher, year, edition, pages
2019. Vol. 14, no 4, article id 044008
Keywords [en]
provisioning ecosystem services, sustainable development, urban transition, machine learning, openly accessible data, informality
National Category
Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:su:diva-168590DOI: 10.1088/1748-9326/aafe43ISI: 000462895800004OAI: oai:DiVA.org:su-168590DiVA, id: diva2:1316898
Available from: 2019-05-21 Created: 2019-05-21 Last updated: 2019-05-21Bibliographically approved

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Citation style
  • apa
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