@article{299,
  author = {Felix Creutzig and Steffen Lohrey and Xuemei Bai and Alexander Baklanov and Richard Dawson and Shobhakar Dhakal and William Lamb and Timon McPhearson and Jan Minx and Esteban Munoz and Brenna Walsh},
  title = {Upscaling urban data science for global climate solutions},
  abstract = {Technical summary Cities have an increasingly integral role in addressing climate change. To gain a common understanding of solutions, we require adequate and representative data of urban areas, including data on related greenhouse gas emissions, climate threats and of socio-economic contexts. Here, we review the current state of urban data science in the context of climate change, investigating the contribution of urban metabolism studies, remote sensing, big data approaches, urban economics, urban climate and weather studies. We outline three routes for upscaling urban data science for global climate solutions: 1) Mainstreaming and harmonizing data collection in cities worldwide; 2) Exploiting big data and machine learning to scale solutions while maintaining privacy; 3) Applying computational techniques and data science methods to analyse published qualitative information for the systematization and understanding of first-order climate effects and solutions. Collaborative efforts towards a joint data platform and integrated urban services would provide the quantitative foundations of the emerging global urban sustainability science.},
  year = {2019},
  journal = {Global Sustainability},
  volume = {2},
  month = {01},
  issn = {2059-4798},
  url = {https://par.nsf.gov/biblio/10110651},
  doi = {10.1017/sus.2018.16},
}