Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking

Stoner, O. , Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020) Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(4), pp. 815-839. (doi: 10.1111/rssc.12428)

Abstract

In 2017 an estimated 3 billion people used polluting fuels and technologies as their primary cooking solution, with 3.8 million deaths annually attributed to household exposure to the resulting fine particulate matter air pollution. Currently, health burdens are calculated by using aggregations of fuel types, e.g. solid fuels, as country level estimates of the use of specific fuel types, e.g. wood and charcoal, are unavailable. To expand the knowledge base about effects of household air pollution on health, we develop and implement a novel Bayesian hierarchical model, based on generalized Dirichlet–multinomial distributions, that jointly estimates non-linear trends in the use of eight key fuel types, overcoming several data-specific challenges including missing or combined fuel use values. We assess model fit by using within-sample predictive analysis and an out-of-sample prediction experiment to evaluate the model's forecasting performance.

• Item Type: Articles
• Additional Information: This work was supported by a Natural Environment Research Council GW4+ doctoral training partnership studentship (NE/L002434/1) and WHO contract APW 201790695.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Stoner, Dr Oliver
• Authors: Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G., and Adair-Rohani, H.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Journal of the Royal Statistical Society: Series C (Applied Statistics)
• Publisher: Wiley
• ISSN: 0035-9254
• ISSN (Online): 1467-9876
• Published Online: 07 July 2020
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 69(4): 815-839
• Publisher Policy: Reproduced under a Creative Commons License