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

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 f...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society Series C: Applied Statistics 2020-08, Vol.69 (4), p.815-839
Main Authors: Stoner, Oliver, Shaddick, Gavin, Economou, Theo, Gumy, Sophie, Lewis, Jessica, Lucio, Itzel, Ruggeri, Giulia, Adair-Rohani, Heather
Format: Article
Language:English
Subjects:
Air pollution
Bayesian analysis
Bayesian hierarchical model
Charcoal
Clean fuels
Cooking
Dirichlet problem
Forecasting
Fuels
Generalized Dirichlet distribution
Households
Knowledge base
Knowledge bases (artificial intelligence)
Original Articles
Predictions
Residential energy
Solid fuels
Trends
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Summary: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.
ISSN:0035-9254
1467-9876
DOI:10.1111/rssc.12428