In the realm of building science, a groundbreaking study led by Ruiji Sun at the Center for the Built Environment, University of California, Berkeley, is challenging conventional wisdom and offering new insights into how we understand and optimize thermal comfort in buildings. The research, published in the journal Indoor Environments, which translates to ‘Indoor Environments’, employs a novel approach to causal inference, using natural experiments to shed light on the complex interplay between indoor environments and occupant satisfaction.
Traditionally, researchers have relied on correlational analysis to understand the relationship between indoor temperatures and occupant comfort. However, as Sun explains, “Correlational analysis, such as linear regression, does not imply causation.” This means that while we might observe a correlation between indoor temperature and thermal comfort, we cannot definitively say that one causes the other.
To address this limitation, Sun and his team turned to a unique natural experiment: China’s winter district heating policy. This policy, which provides heating to cities north of the Huai River based on their geographical locations, creates a situation where cities near the river are similar in almost every way except for the availability of district heating. This natural threshold allows researchers to use a method called regression discontinuity to isolate the causal effects of district heating on indoor environments and occupant comfort.
The findings are striking. The study reveals that district heating increases mean indoor operative temperatures by 4.3°C and makes occupants feel 0.6 degrees warmer on average. However, the relationship between indoor temperature and thermal satisfaction is not as straightforward as one might expect. Sun notes, “We show that the indoor operative temperature could be either positively or negatively correlated with occupants’ thermal satisfaction. However, we cannot conclude that increasing the indoor operative temperature in these circumstances will necessarily lead to higher or lower thermal satisfaction.”
This research has significant implications for the energy sector. By providing a more nuanced understanding of thermal comfort, it could help guide the development of more efficient and effective heating strategies. For instance, rather than simply increasing indoor temperatures, building managers could focus on optimizing other aspects of the indoor environment, such as air quality or humidity, to improve occupant comfort and satisfaction.
Moreover, the study highlights the importance of causal inference methods in building science. As Sun and his team demonstrate, these methods can provide valuable insights that correlational analysis alone cannot. This could pave the way for more rigorous and reliable research in the field, ultimately leading to better-designed buildings and more satisfied occupants.
The implications of this research extend beyond the energy sector. In an era of climate change and increasing energy costs, understanding how to optimize thermal comfort in buildings is more important than ever. By providing a new framework for causal inference in building science, Sun’s research could help shape the future of building design and energy management. As we continue to grapple with these challenges, studies like this one will be invaluable in guiding our efforts to create more sustainable and comfortable built environments.
