Abstract

The UN Sustainable Development Goals (SDGs) have an important subnational focus: the principle of leaving no one behind, the transformative foundation of the 2030 Agenda, also means leaving no place behind. Consequently, the UN SDGs include several indicators that incorporate measurements for urban and rural areas. For example, on average, access to health care may be good at the national level, but it may be less satisfactory in some of the rural areas, while urban population are more likely to be exposed to air pollution.
Even if indicators are measured in an identical manner, they might not be comparable if they are applied to territories that are not uniformly defined. Without a harmonised global definition, comparisons of urbanisation and indicators for rural areas, urban areas and cities are difficult to interpret as the differences in definitions may bias the results.
In 2020, the UN Statistical Commission endorsed a harmonised definition of cities, towns and rural areas for international comparison, called the Degree of Urbanisation. This new method based on a population grid allows for a harmonised comparison of rural areas across the globe. One benefit of the population grid is that it uses spatial units of the same shape (squares) and size across the entire world. It also allows identifying settlements without having to rely on other indicators such as population size or population density of census units.
In this presentation, Eurostat will first introduce the degree of urbanisation methodology and its two extensions. Secondly, Eurostat will demonstrate the use of this methodology to measure SDG indicators in the European context. Currently, Eurostat disseminates close to 200 datasets using the Degree of Urbanisation breakdown and 20 of these are directly related to measuring progress towards SDGs.
The Degree of Urbanisation methodology is intended to facilitate the comparison across national borders to complement the national definitions and not to replace them. National definitions can incorporate local data that may not be available globally and incorporate important country specificities.