Advanced

Smart planet governance : analyzing the role of big data for monitoring the Sustainable Development Goals

Bauer, Tim LU (2018) In Master Thesis Series in Environmental Studies and Sustainability Science MESM02 20181
LUCSUS (Lund University Centre for Sustainability Studies)
Abstract
Achieving the 17 Sustainable Development Goals (SDGs) depends on timely and reliable information. However, standardized methodologies and regularly available data exist for only 40% of the 232 indicators. The “Data Revolution for Sustainable Development” is supposed to bridge these data gaps by
harnessing ongoing developments in information technology, particularly big data. Currently existing pilots draw on sources such as satellite imagery, mobile phone data, and social media to calculate SDG indicators related to poverty, hunger, and health in real-time and disaggregated by demographic
groups and locations. These approaches represent a new mode of knowing about people and planet whose consequences are yet to be understood. This work... (More)
Achieving the 17 Sustainable Development Goals (SDGs) depends on timely and reliable information. However, standardized methodologies and regularly available data exist for only 40% of the 232 indicators. The “Data Revolution for Sustainable Development” is supposed to bridge these data gaps by
harnessing ongoing developments in information technology, particularly big data. Currently existing pilots draw on sources such as satellite imagery, mobile phone data, and social media to calculate SDG indicators related to poverty, hunger, and health in real-time and disaggregated by demographic
groups and locations. These approaches represent a new mode of knowing about people and planet whose consequences are yet to be understood. This work draws on the concept of cognitive assemblages to argue that representational technologies have knowledge and governance effects in terms of how problems are understood and addressed. It argues that inherent affordances of big data could exacerbate and obscure the knowledge and governance effects of the SDG indicator system and proposes six possible future trajectories. On this base, a combination of text mining and document analysis is used to identify and investigate 22 indicators that are currently supported by various big data approaches. It is shown that the uptake of big data in the SDGs is gradual and irregular rather than revolutionary. The inherent tendency of big data towards all-encompassing vision raises questions for the understanding of sustainability in the Anthropocene. (Less)
Please use this url to cite or link to this publication:
author
Bauer, Tim LU
supervisor
organization
course
MESM02 20181
year
type
H2 - Master's Degree (Two Years)
subject
keywords
sustainability science, SDGs, indicator governance, big data, assemblage
publication/series
Master Thesis Series in Environmental Studies and Sustainability Science
report number
2018:022
language
English
id
8947430
date added to LUP
2018-06-10 13:46:36
date last changed
2018-06-10 13:46:36
@misc{8947430,
  abstract     = {Achieving the 17 Sustainable Development Goals (SDGs) depends on timely and reliable information. However, standardized methodologies and regularly available data exist for only 40% of the 232 indicators. The “Data Revolution for Sustainable Development” is supposed to bridge these data gaps by
harnessing ongoing developments in information technology, particularly big data. Currently existing pilots draw on sources such as satellite imagery, mobile phone data, and social media to calculate SDG indicators related to poverty, hunger, and health in real-time and disaggregated by demographic
groups and locations. These approaches represent a new mode of knowing about people and planet whose consequences are yet to be understood. This work draws on the concept of cognitive assemblages to argue that representational technologies have knowledge and governance effects in terms of how problems are understood and addressed. It argues that inherent affordances of big data could exacerbate and obscure the knowledge and governance effects of the SDG indicator system and proposes six possible future trajectories. On this base, a combination of text mining and document analysis is used to identify and investigate 22 indicators that are currently supported by various big data approaches. It is shown that the uptake of big data in the SDGs is gradual and irregular rather than revolutionary. The inherent tendency of big data towards all-encompassing vision raises questions for the understanding of sustainability in the Anthropocene.},
  author       = {Bauer, Tim},
  keyword      = {sustainability science,SDGs,indicator governance,big data,assemblage},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master Thesis Series in Environmental Studies and Sustainability Science},
  title        = {Smart planet governance : analyzing the role of big data for monitoring the Sustainable Development Goals},
  year         = {2018},
}