Funding - C.A.V.'s research at the University of Leeds (UoL) was supported by GIZ grant 'Solid waste flow diagram tool and ISWM benchmark indicator monitoring for cities'. The relevant WABIs web-portal was funded by EPSRC impact acceleration fund (UoL internal allocation, via the 'Cities' cross-university theme) and Wasteaware Ltd. in-kind contribution. Acknowledgements - We are grateful to UN-Habitat for funding the work on the initial version of the indicators, including 20 city profiles, and GIZ through their 'Operator Models' project which funded an intermediate version of the indicators and a further 5 city profiles. We acknowledge all the profilers of the individual cities – many are named in Table S2, and others in reference (Scheinberg et al. 2010). We thank past MSc students under the authors' supervision for offering preliminary partial data clearing and commentary: Henry Hickman (MSc dissertation at University of Leeds, supervised by C.A.V and D.C.W.) and Margaux Fargier (Final year MEng dissertation at Imperial College London, supervised by S.M.G, D.C.W and C.A.V.). We are grateful to Dr Josh Cottom and Mr Ed Cook at the University of Leeds for input on the GDP version selection. We acknowledge the support of Dr Ljiljana Rodic for contributing in data quality control.

Here you can find the input and summary output datasets and the analysis protocol and R code associated with the publciation. The independent variables dataset analysed here refer to specific indicators of the WABI methodology (https://www.sciencedirect.com/science/article/pii/S0956053X14004905) that generates solid waste management and resource recovery profiles for cities. It was applied here for 40 cities around the world. Input file: Metadata info used by R codes Full data set for the WABI, used by the R codes Data required for plotting the map in Figure 1 Summary output file: Metadata info used by R codes Summary of results for two modelling approaches (machine learning: Conditional random-forest and non-linear regression)