Through this joint initiative, Nexleaf Analytics and Rural Women Energy Security (RUWES), with support from CCAC, set out to reimagine how we tackle household air pollution. Rather than focusing on changing deeply-entrenched and culturally-driven behaviors of local communities, we used data to understand household behavioral patterns (adoption) to guide the pilot and ultimately learn which cooking solutions are worth scaling up.

Every year, four million people die from illnesses attributable to household air pollution. Black carbon (BC) is the second largest contributor to global warming, and emissions from traditional cooking practices are a significant global source of BC. For these reasons, changing how people cook has become a major public health and climate change priority.

The world rallied around a solution: introduce cleaner-burning, manufactured cookstoves for the rural poor. The only question that remained was how to make these “improved cookstoves” affordable for people who live on less than $2 a day…or so we thought.

This project lays the groundwork for sensor-enabled results based financing for improved cookstoves globally. A tremendous amount!of work was accomplished in developing new sensor integrated wireless platforms, climate and health metrics that can be implemented in a real-time platform for results-based financing, an open data platform, consensus across diverse  stakeholders on precise frameworks for monitoring, and a large amount of sensor data from rural households across four states in India.

In 2019, Nexleaf applied Stovetrace to liquid fuel stoves— LPG, ethanol, and ethanol gel—for the first time as part of a pilot project in partnership with Rural Women Energy Security (RUWES) and with support from the Climate and Clean Air Coalition (CCAC). The pilot involved the distribution of 5 stove types to 50 women (10 women per stove) and a 6-month monitoring period using Stovetrace. The pilot aimed to identify the most reliable stoves for rural Nigerian women, and offered valuable lessons-learned on how sensors can play a role in clean cooking data collection.