Despite the large potential economic and environmental impacts of increased air conditioning usage (IEA, 2018), there is little direct empirical evidence on this topic, much less analytical modelling of potential policy impacts. Evidence is particularly scant in low and middle-income countries where most of the growth in air conditioning is expected to occur (Gertler, et al, 2016).
In this project, the researchers aim to use household microdata to develop new measures of air conditioning potential in IGC partner and flexible engagement countries in Africa and South Asia. The broader goal is to understand how and when households and firms adopt air conditioning. Evidence on this question exists for the United States (Biddle, 2008) and Mexico (Davis and Gertler, 2015), describing empirically how air conditioner adoption increases as household incomes rise and how they rise differentially across climates of differing temperatures. However, little work has been done to estimate this same relationship in low and middle-income countries around the world.
There is also a knowledge gap about the impact air conditioning adoption will have on electricity markets. A typical air conditioner uses 20 times as much electricity as a ceiling fan, and 100 times as much electricity as an LED lightbulb. Thus, widespread adoption of air conditioning will require large economic investments in electricity generation and transmission infrastructure.
Another critical knowledge gap is the lack of reliable research on the impact of air conditioning adoption on the environment. Most electricity worldwide continues to be generated using fossil fuels. Thus growing air conditioner adoption means billions of tons of increased carbon dioxide emissions. In addition, the refrigerants used in air conditioning are themselves a potent greenhouse gas. Understanding the pace of growth in air conditioner sales is crucial for understanding the recent Kigali Agreement, which seeks to reduce significantly the use of hydrofluorocarbons (HFCs). This project aims to fill this important gap in generating predictions about future environmental impacts under different policy scenarios