Heterogeneity and scale of sustainable development in cities

Abstract

Rapid worldwide urbanization is at once the main cause and, potentially, the main solution to global sustainable development challenges. The growth of cities is typically associated with increases in socioeconomic productivity, but it also creates strong inequalities. Despite a growing body of evidence characterizing these heterogeneities in developed urban areas, not much is known systematically about their most extreme forms in developing cities and their consequences for sustainability. Here, we characterize the general patterns of income and access to services in a large number of developing cities, with an emphasis on an extensive, high-resolution analysis of the urban areas of Brazil and South Africa. We use detailed census data to construct sustainable development indices in hundreds of thousands of neighborhoods and show that their statistics are scale-dependent and point to the critical role of large cities in creating higher average incomes and greater access to services within their national context. We then quantify the general statistical trajectory toward universal basic service provision at different scales to show that it is characterized by varying levels of inequality, with initial increases in access being typically accompanied by growing disparities over characteristic spatial scales. These results demonstrate how extensions of these methods to other goals and data can be used over time and space to produce a simple but general quantitative assessment of progress toward internationally agreed sustainable development goals.

Keywords: inequality; neighborhoods; slums; spatial correlations; urban services.

Fig. 1.

Heterogeneity and scale of sustainable development in cities. (A) The Sustainable Development Index, X_i, at the subplace level for all of South Africa, the Johannesburg MA, and a single subplace. (B) The values of the Gini coefficient for income across scales for South Africa. The median across all units of analysis within a class is shown by a horizontal black line, with the 25th to 75th percentiles shown by the gray box. (C) Development priorities identified by slum residents in the 10 countries in Table 1.

Fig. 2.

Agglomeration effects and heterogeneity of sustainable development in cities of Brazil and South Africa. (A) Scaling of personal income with population for Brazil (squares) and South Africa's MAs (triangles). The yellow line shows the theoretical slope of 1 + 1/6 (35), the red shows the best fit, and the gray line shows the 1:1 line for reference. The best fit demonstrates that larger cities have on average higher resources per capita, at least in nominal terms, which can be invested in sustainable development. (B) The relationship between the SD, ${\sigma }_i$, and mean, ${\overline{X}}_i$, of the Sustainable Development Index, X_i, for Brazil's 38 MAs (purple) and 207 South African municipal regions (orange), where the size of the circle is proportional to population. The two black lines bind the area in which (${\overline{X}}_i,{\sigma }_i$) pairs can exist, with the upper curved line showing maximal inequality (Kuznets curve, b = 1) and the horizontal line corresponding to total equality (b = 0). The dashed line shows the estimated best fit for the mean heterogeneity index b. (C) The decomposition of X_i into subcomponents: X_i^electricity (orange), X_i^water (blue), X_i^sanitation (green), and X_i^homes (purple) for Brazil's metropolitan regions, which allows us to see the role of larger cities at providing improved services. (D) The positive association between Moran’s I (distance threshold = 5km) and ${\sigma }_i$ for South African municipalities, showing that higher spatial clustering is associated with higher inequality of access to services. (Inset) The variation of Moran’s I with the distance threshold s for South Africa’s major metropolitan regions.