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

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  • Oliver Stoner
  • Gavin Shaddick
  • Theo Economou
  • Sophie Gumy
  • Jessica Lewis
  • Itzel Lucio
  • Giulia Ruggeri
  • Heather Adair‐Rohani

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.

Suggested Citation

  • Oliver Stoner & Gavin Shaddick & Theo Economou & Sophie Gumy & Jessica Lewis & Itzel Lucio & Giulia Ruggeri & Heather Adair‐Rohani, 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, Royal Statistical Society, vol. 69(4), pages 815-839, August.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:4:p:815-839
    DOI: 10.1111/rssc.12428
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    References listed on IDEAS

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    1. Wood, Simon N., 2016. "Just Another Gibbs Additive Modeler: Interfacing JAGS and mgcv," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i07).
    2. Gavin Shaddick & Matthew L. Thomas & Amelia Green & Michael Brauer & Aaron van Donkelaar & Rick Burnett & Howard H. Chang & Aaron Cohen & Rita Van Dingenen & Carlos Dora & Sophie Gumy & Yang Liu & Ran, 2018. "Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(1), pages 231-253, January.
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    Cited by:

    1. Francesco Lolli & Antonio Maria Coruzzolo & Samuele Marinello & Asia Traini & Rita Gamberini, 2022. "A Bibliographic Analysis of Indoor Air Quality (IAQ) in Industrial Environments," Sustainability, MDPI, vol. 14(16), pages 1-26, August.

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