Energy-efficient technology retrofit investment behaviors of households in lower and higher income regions

Urban regions consume approximately 65% of all energy produced and emit 70% of the CO2 to the environment. Buildings, specifically, consume approximately 40% of energy in developed countries and emit nearly 40% of CO2. Most of this energy is used for heating, cooling, and lighting end uses. Since approximately half of building energy use is attributed to residential buildings in the U.S., improving their energy efficiency will help to reduce energy use substantially, as well as benefit households through reduced energy costs. However, little effort has focused on understanding how energy efficiency investments are made, particularly across different socioeconomic groups. Using data for residential buildings in Cedar Falls, Iowa, including energy efficiency investment data for a utility rebate program, assessors data, and U.S. Census data, residential energy efficiency investments are studied in three stages using different subdivisions of the datasets through multistage sampling analysis. Frequency analysis, correlation analysis, and principal component analysis are used to study household investment behavior in (Stage 1) the overall dataset for the city, (Stage 2) the lowest and highest income census tracts, and (Stage 3) a subset of similar housing units in the lowest and highest income census tracts. Specifically, energy efficiency investments in efficient lighting, air conditioners, furnaces, and insulation are studied. Overall, for residential buildings in this region, efficient lighting was the most invested in technology, followed by air conditioners and furnaces, and finally, insulation. If grouping air conditioners and furnaces together, HVAC systems are the most common investment. Interestingly, air conditioners and furnaces are, by far, the most expensive technology to invest in, compared to most other energy-efficient technologies used in homes, yet they are among the most common types of investments. They also appear to be an entry point to investing in energy efficiency, as most households purchasing the studied HVAC systems have not previously utilized the available utility rebates. In addition, it is important to note that the most typical scenario for investment is due to the HVAC system is broken, irrespective of age and/or of the HVAC unit. For the study of efficiency investments in housing units in the lowest and highest income tracts, overall, there were more efficiency investments per housing unit and in total in the high-income areas as compared to the lower-income areas. There were also differences in the type of investments. The higher income tract prioritized efficient lighting and HVAC systems as investments, similar to the overall dataset, while the lower-income tract invested most in HVAC systems followed by insulation. Some of this variation in the type of investment may be because the lower-income areas generally include older housing units, which may not be built to the modern energy code requirements. Insulation investments are also generally lower in cost compared to HVAC systems, which may be a more feasible investment for low-income households. Correlation analysis and Principal Component Analysis (PCA) results suggest two main findings. First, the cooling capacity of the air conditioners invested is most driven by housing age for the lower-income housing units and correlated most, but to a lesser extent, with housing size for the higher-income housing units. Second is that lower-income household\u27s investment was higher proportional to cooling capacity and efficiency, thus resulting in higher rebate amounts as compared to the higher-income households, which have lower correlations with all variables. In other words, for the housing units that made investments in the lower-income regions, they invested more money in higher efficiency systems, as compared to the higher income regions who made more investments in the characteristics of the air conditioner chosen and the associated rebate were not significant factors that influenced such investments. This suggests that the policies developed for rebate programs more strongly influence lower-income households, which have less available monetary resources to make investments. Holding age and size of the housing unit constant in the highest and lowest income tracts (Stage 3), correlation analysis for air conditioner investments also shows that lower-income households investment is more strongly associated with efficiency compared to the higher-income households, meaning lower-income households made higher investments to increase the air conditioners efficiency. These findings highlight the importance of policies and incentive programs that focus on low income and high-income housing units and the variations in investment behavior. This research can help to improve these programs through a better understanding of types, quantities, and influential factors impacting varied income levels differently