Overview
Rural electrification remains a critical development challenge in low-income countries, with roughly 1.3 billion people still living without reliable electricity. As conventional energy systems in the developing world rely heavily on fossil fuels, and traditional appliances utilize energy inefficiently, the promotion of more efficient appliances may help increase access to electricity for households while minimizing environmental costs. Still, accurately anticipating how increased adoption of energy efficient appliances will impact energy consumption is complicated by a “rebound effect” – the potential for households to increase their overall energy consumption in response to an improvement in appliance efficiency.
Project Vision and Strategy
This project capitalizes on a randomized evaluation of innovative solar microgrids in India, along with novel electronic data collection methods, to address important questions related to energy efficiency. By combining real-time energy use data (captured by “smart meters”) with comprehensive household survey data, the research team will be able to infer which types of applications users are powering at different times, thus revealing how an improvement in lighting efficiency affects not only demand for lighting (i.e. “direct rebound”), but also demand for other end uses (such as cell phone charging or cooling)
Updates
In 2014, their team in India will distribute LED lightbulbs (which cost twice as much as CFLs, but provide the same lighting with less than half as much electricity) to 400 randomly selected households in rural Rajasthan, all of whom will have been recently connected to a Gram Power microgrid. An additional 400 households will serve as a “control” group, and will receive CFL bulbs. They will visit each of the 800 treatment and control households once to collect self-reported appliance use data, which will allow them to refine our data disaggregation approach when compared with the high-resolution “smart meter” data.
Lead Researchers
- Professor Catherine Wolfram, Haas School of Business, UC Berkeley
- Professor Eric Brewer, Electrical Engineering and Computer Science, UC Berkeley
- Professor Meredith Fowlie, Agricultural and Resource Economics, UC Berkeley
- Professor Edward Miguel, Economics, UC Berkeley