Using Cluster Analysis to Assess the Adoption of Energy Star Ratings by the Commercial Buildings in the United States

As of December 2013, approximately 5,200 building managers and owners in the commercial building sector in the United States have decided to retrofit their buildings and seek qualification for the Energy Star rating administered by the U.S. Environmental Protection Agency. I have sampled the buildings that have scored high enough, at least 75 on a scale of 100, and sent out surveys asking various questions regarding the decision factors to retrofit their buildings. I then used principal components analysis to factor out the primary reasons why building managers/owners implement various clean energy projects and what factors contribute to making that decision. After that I used cluster analysis to explain how homogeneous and heterogeneous the decision-making processes are. Finally, I used regression analysis to find the relationships between the principal components and size variables such as the size of the buildings and the total number of full-time employees. The results show that, in general, the majority (56%) of the managers/owners in the commercial building sector are not concerned about the energy efficiency of their buildings, 27% of them are low or moderately concerned and only 17% of them are highly concerned about energy efficiency and energy conservation. The results also show that when the building size or the total number of employees goes up then the concern for energy efficiency, insulation upgrade, and resource conservation go down. In addition, the result shows an interaction effect of the building size and total number of employment. It shows that when these variables are multiplied together then the resultant value is positively related to concern for energy efficiency, insulation upgrade, and resource conservation.