Energy Efficiency Technology Adoption Forecasting
Overview
Company: Opinion Dynamics
Role: Senior consultant
Methods: surveys, quantitative & cluster analyses
Skills: survey design, quantitative data analysis, synthesis & writing
Tools: Qualtrics, Jibunu, SPSS, Excel, PPT
Deliverables: report & slide deck
Impact: forecasted technology adoption & characterization of purchaser types for CA energy stakeholders & regulators
I helped design and implement a study that characterized the energy efficiency technology market in California. I examined consumers’ financial and environmental attitudes, preferences, and likelihood to adopt energy efficient technologies, such as heat pumps, smart thermostats, lighting controls, and efficient refrigerators.
Data collection included email and mail-push-to-web surveys with California residents, multifamily building owners and managers, and businesses. I found that residential consumers can be segmented into distinct groups with varying purchasing behaviors, rebates move the meter for technology adoption for all sectors, on-bill financing is seemingly less effective for incentivizing energy efficient purchases, and customers perceive the environmental impacts of their purchases as important in their decision-making.
Objectives
Estimate the future customer adoption levels of new energy efficiency technologies across all building sectors.
Research customer behavior and preferences and better inform the energy efficiency forecast via primary data collection in the residential and commercial sectors.
Provide insights into customer preferences on both economic and non-economic factors and other characteristics that influence customers’ willingness to adopt energy efficiency technologies.
Hypothesis
Economics are not the only driver of technology adoption behavior, and in some cases, may not even be the primary driver.
Approach
To collect primary data, I designed and administered 3 web surveys.
A residential survey based on a sample of non-low-income California residents who made decisions about technologies in their home. Survey recruitment was done via email and mail; the survey response rate was 13%.
A multifamily survey of California owners and managers of residential properties who made decisions about technologies in their respective units. Survey recruitment was done via mail; the survey response rate was 7%.
A commercial survey of business owners or employees who made decisions about equipment in their respective facilities. Survey recruitment was done via email and mail; the survey response rate was 6%.
I asked surveyed customers about their:
Technology, building, and general characteristics.
Energy efficiency program awareness and participation.
Environmental, energy, and financial attitudes.
Motivations, barriers, and willingness to purchase energy efficient technologies.
Feedback on how the COVID-19 pandemic has affected their decisions and plans.
Analyses
I performed descriptive statistics in SPSS to explore the frequencies and dispersion of responses across each survey topic. “Value factors” were created to aggregate elements that customers consider in their decision-making. I created these value factors by combining, normalizing, and averaging customer responses to scaled survey questions.
Cluster analyses were conducted to divide residential respondents into distinct customer segments based on their answers. I performed additional cross-tabulation and statistics to compare likelihood-to-adopt responses against other variables.
Findings
Willingness-to-Adopt
Frequencies and cross-tabulations revealed, unsurprisingly, that likelihood to adopt increases as payback period decreases due to rebate offerings. An example technology is shown above in the bar chart, where likelihood to adopt is weighed against rebate level and payback period.
The last bar demonstrates likelihood to adopt a smart thermostat when on-bill financing is offered. While this chart only shows one type of technology, most products revealed a similar trend: likelihood to purchase a technology increases as the rebate increases, but drops if financing is offered instead.
Value Factors
Residential and commercial respondents reported that in general, “Eco Impacts” are the most important factor in their purchase decision-making. Also important but to a lesser extent, the “Lifetime Costs” of a technology are considered significant.
The “Hassle Factor” (ease of purchase, installation, and use), “Non-Consumption Performance” (aesthetics and features of technology), and “Social Signaling” (perceived eco or innovative nature of technology) value factors were reported as moderately important in decision-making. I found these trends were consistent across all customer clusters, business segments, and technologies asked about in the surveys.
COVID-19 Impacts
I discovered that the majority of customers have experienced a negative impact on their everyday life due to COVID-19. Despite this impact, most respondents noted their financial situation has not and likely will not change.
When compared with value factors, I found that respondents perceive the Hassle Factor of purchasing and installing a technology as more important now than before COVID-19. This implies they are considering how comfortable they are with a contractor entering their building during the pandemic. Respondents also reported the pandemic raised the importance of Upfront and Lifetime Costs in their purchase decisions.
Takeaways
This adoption study was extensive in scope, therefore I cannot discuss all implications here. But a few highlights include:
The residential cluster analysis revealed four distinct customer segments: “Average Californians” (average levels of concern across the value factors), “Eager Adopters” (higher than average levels of environmental concern and financial well-being), “Likely Laggards” (lower than average levels of environmental concern and financial well-being), and “Economically Strained Environmentalists” (higher than average levels of environmental concern but lower than average levels of financial well-being). These segments provide important insight into various customer purchasing behaviors, which could prove useful for targeted marketing campaigns among utilities that offer rebates and financing for energy efficient technologies.
Residents and businesses find the uncertainty of energy savings as the largest barrier for adopting energy efficiency technologies. This suggests that program administrators, utilities, and contractors who provide energy efficiency education and incentives should focus on discussing the importance of proper operation and maintenance of energy efficiency technology to ensure reliable and long-lasting energy savings.
Multifamily building owners reported that possible disruptions to tenant units is the biggest barrier for upgrading to energy efficient technologies. This tells us that building sectors have different perceived barriers of upgrading to energy efficiency, independent of upfront cost.
The adjusted technology willingness-to-adopt calculations of this study will be used as data inputs in a larger energy efficiency forecasting model, which was not discussed in this writeup.
Study limitations include self-reporting and lack of technology penetration and saturation data.