@book{30205,
  keywords = {Biomass, Smoke, Combustion, Charcoal, Marketing},
  author = {Silvia Hostettler and Eileen Hazboun and Jean-Claude Bolay and Daniel L Wilson and Mohammed Idris Adam and Omnia Abbas and Jeremy Coyle and Angeli Kirk and Javier Rosa and Ashok J Gadgil},
  title = {Comparing Cookstove Usage Measured with Sensors Versus Cell Phone-Based Surveys in Darfur, Sudan},
  abstract = {<p>Three billion people rely on combustion of biomass to cook their food, and the resulting air pollution kills 4&nbsp;million people annually. Replacing inefficient traditional stoves with “improved cookstoves” may help reduce the dangers of cooking. Therefore analysts, policy makers, and practitioners are eager to quantify adoption of improved cookstoves. In this study, we use 170 instrumented cookstoves as well as cellphone-based surveys to measure the adoption of free-of-charge Berkeley-Darfur Stoves (BDSs) in Darfur, Sudan where roughly 34,000 BDS have been disseminated. We estimate that at least 71&nbsp;% of participants use the stove more than 10&nbsp;% of days that the sensor was installed on the cookstove. Compared to sensor-measured data, surveyed participants overestimate adoption both in terms of daily hours of cooking and daily cooking events (<em class="EmphasisTypeItalic ">p</em>&nbsp;&lt;&nbsp;0.001). Average participants overreport daily cooking hours by 1.2&nbsp;h and daily cooking events by 1.3 events. These overestimations are roughly double sensor-measured values. Data reported by participants may be erroneous due to difficulty in recollection, courtesy bias, or the desire to keep personal information obscure. A significant portion of sensors was lost during this study, presumably due to thermal damage from the unexpected commonality of charcoal fires in the BDS; thus pointing to a potential need to redesign the stove to accommodate users’ desire to cook using multiple fuel types. The cooking event detection algorithm seems to perform well in terms of face validity, but a database of cooking logs or witnessed accounts of cooking is absent; the algorithm should be trained against expert-labeled data for the local cooking context to further refine its performance.</p>},
  year = {2015},
  journal = {Technologies for Development},
  pages = {211 - 221},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-319-16246-1},
  doi = {10.1007/978-3-319-16247-8_20},
  language = {eng},
}