Abstract
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.
Highlights
Introduction cri ptAs countries develop and many societies transition to urbanisation, they tend to reduce their dependence on the local natural environment to meet basic needs (Anderson 1987, Cumming et al 2014, Sanderson et al 2018)
In contrast to the common framing of natural resource dependence as a dichotomous category, we propose a conceptualization of dependence as a gradient (Figure 1)
We investigate the explanatory power of the input variables (E1 – E8) by applying machine learning (Willcock et al 2018)
Summary
As countries develop and many societies transition to urbanisation, they tend to reduce their dependence on the local natural environment to meet basic needs (Anderson 1987, Cumming et al 2014, Sanderson et al 2018). This decoupling is often described by the transition from a strong reliance on agriculture as the main source of national income to other, less directly coupled sectors such as industrial and service sectors (Daunton 1995, Mellor 1995, Soubbotina and Sheram 2000). How cities balance rapid urbanisation in relation to resource consumption may determine whether or not many of the Sustainable Development Goals (SDGs) are achieved (UN 2015)
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