A Systematic Review to Evaluate the Association between Clean Cooking Technologies and Time Use in Low- and Middle-Income Countries

Int J Environ Res Public Health. 2019 Jun 27;16(13):2277. doi: 10.3390/ijerph16132277.

Abstract

Interventions implementing clean fuels to mitigate household air pollution in low- and middle-income countries have focused on environmental and health outcomes, but few have evaluated time savings. We performed a systematic review, searching for studies of clean fuel interventions that measured time use. A total of 868 manuscripts were identified that met the search criteria, but only 2 met the inclusion criteria. Both were cross-sectional and were conducted in rural India. The first surveyed the female head of household (141 using biogas and 58 using biomass) and reported 1.2 h saved per day collecting fuel and 0.7 h saved cooking, resulting in a combined 28.9 days saved over an entire year. The second surveyed the head of household (37 using biogas and 68 using biomass, 13% female) and reported 1.5 h saved per day collecting fuel, or 22.8 days saved over a year. Based on these time savings, we estimated that clean fuel use could result in a 3.8% or 4.7% increase in daily income, respectively, not including time or costs for fuel procurement. Clean fuel interventions could save users time and money. Few studies have evaluated this potential benefit, suggesting that prospective studies or randomized controlled trials are needed to adequately measure gains.

Keywords: Cooking; air pollution; biomass; stoves; time; wage.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Systematic Review

MeSH terms

  • Air Pollution, Indoor / analysis*
  • Air Pollution, Indoor / statistics & numerical data*
  • Cooking / methods*
  • Cooking / statistics & numerical data*
  • Cross-Sectional Studies
  • Developing Countries / statistics & numerical data
  • Environmental Exposure / statistics & numerical data*
  • Female
  • Humans
  • India
  • Male
  • Poverty / statistics & numerical data*
  • Prospective Studies
  • Rural Population / statistics & numerical data*
  • Socioeconomic Factors
  • Time Factors