Comparing Cookstove Usage Measured with Sensors Versus Cell Phone-Based Surveys in Darfur, Sudan
Three billion people rely on combustion of biomass to cook their food, and the resulting air pollution kills 4 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 % of participants use the stove more than 10 % 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 (p < 0.001). Average participants overreport daily cooking hours by 1.2 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.
KeywordsSUMs Data Event Detection Algorithm Previous Minimum Traditional Stove Internally Displace People
Authors gratefully acknowledge financial support from multiple sources for this work. The primary funding for this work comes from a DIV Phase-1 grant by the United States Agency for International Development (USAID) to Potential Energy. Daniel Wilson and Angeli Kirk are grateful for support from the National Science Foundation (NSF) Graduate Research Fellowship. Additional funding for personnel and materials for this project has been generously provided by funding agencies including the Development Impact Lab (USAID Cooperative Agreement AID-OAA-A-13-00002) which is part of the USAID Higher Education Solutions Network, the Blum Center for Developing Economies, a Behavioral Sensing Grant from The Center for Effective Global Action (CEGA), and Department of Energy Contract DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory (LBNL), operated by the University of California.
- Berkeley Air Monitoring Group (2013). Monitoring and evaluation of the Jiko Poa cookstove in Kenya Report. http://www.washplus.org/sites/default/files/kenya-jiko_cookstove.pdf. Accessed March 1, 2014.
- Burwen, J. (2011). From technology to impact: Understanding and measuring behavior change with improved biomass cookstoves. UC Berkeley: Energy and Resources Group.Google Scholar
- Edwards, A. L. (1957). The social desirability variable in personality assessment and research. New York: Dryden.Google Scholar
- Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., & Borriello, G. (2010). Open data kit: Tools to build information services for developing regions. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development. Article No. 18.Google Scholar
- Karlan, D., McConnell, M., Mullainathan, S., & Zinman, J. (2010). Getting to the top of mind: How reminders increase saving. New Haven: Working Paper, Department of Economics, Yale University.Google Scholar
- Landsberger, H. A. (1958). Hawthorne revisited. Ithaca, New York: Cornell University.Google Scholar
- Lim, S. S., Vos, T., Flaxman, A. D., Danei, G., Shibuya, K., et al. (2013). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the global burden of disease study 2010. The Lancet, 380(9859), 2224–2260.CrossRefGoogle Scholar
- Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.Google Scholar
- Prestwich, A., Perugini, M., & Hurling, R. (2009). Can the effects of implementation intentions on exercise be enhanced using text messages? Psychology and Health, 24(6). doi: 10.1080/08870440802040715.
- Ruiz-Mercado, I., Canuz, E., Walker, J. L., & Smith K. R. (2013). Quantitative metrics of stove adoption using stove use monitors (SUMs). Biomass and Bioenergy, 57:136–148. http://dx.doi.org/10.1016/j.biombioe.2013.07.002.
- World Bank. (2011). Household cookstoves, environment, health, and climate change. Washington, DC: The World Bank.Google Scholar