# Global Household Energy Model: A Multivariate Hierarchical Approach to Estimating Trends in the Use of Polluting and Clean Fuels for Cooking

## TL;DR

This paper introduces a Bayesian hierarchical model to accurately estimate trends in household fuel use for cooking, addressing data gaps and improving health impact assessments of household air pollution.

## Contribution

It develops a multivariate hierarchical approach using Generalized Dirichlet Multinomial distributions to estimate detailed fuel use trends over time.

## Key findings

- Model accurately captures non-linear trends in fuel use.
- Effective handling of missing and combined fuel data.
- Demonstrates improved forecasting of household fuel trends.

## 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 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 impacts of household air pollution on health, we develop and implement a 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 using within-sample predictive analysis and an out-of-sample prediction experiment to evaluate the model's forecasting performance.