Big data to support sustainable development goals (sdgs)

Abstract

This paper discusses the importance of research design and indicators’ selection to facilitate the assessment of the Sustainable Development Goals (SDGs). From this perspective, it feeds the discussion on the need of relevant indicators for monitoring SDGs. It provides a starting point of what can be done to strengthen the scientific underpinning of sustainability indicators. It leverages on findings of authors’ previous empirical studies on SDG indicators and composite indexes. These studies call for some shifts in the SGDs agenda in order to avoid the great risk to misallocate development investments. The first shift derives from the incompatibilities of some SGDs indicators. In particular, trade-offs occur across SDGs: progress on one the economic pillar cannot fully offset lack of progress on another (e.g. rising environmental degradation). This misalignment can be explained referring to the level of analysis of sustainability: part of the problem is that sustainability cannot be addressed solely at the national level as complex interactions among political and governmental levels in complex nested subsystems affect it. This implies a reframing of the SDG framework and a conceptualization of it at a local level to make it more locally relevant. Thus, the paper discusses the potential of big data (spatial information inherent in earth observational data, satellite data, mobile and social media data, etc.) to supplement traditional indicators in SDGs assessment by providing disaggregated geographical information.