The retrofit of buildings to improve energy efficiency represents a vast opportunity in the construction market with the potential to reduce energy demand, create jobs, and achieve environmental benefits. Multiple challenges need to be overcome to unleash this potential and realize the benefits of investments in building energy retrofit projects. Small-/medium-sized commercial buildings are typically underserved by the energy retrofit industry due to the lack of owners’ ability to manage and finance energy efficiency improvements. In the case of energy-intensive and low-margin types of commercial buildings such as restaurants, the potential for energy savings is high, but the favorable conditions that enable retrofits, such as a pending renovation, propensity of clients to conserve energy, and willingness to take time for a retrofit, are scarce. In this research, the development of small energy efficiency projects is examined by proposing a screening method to find prospective candidates for financed energy efficiency projects in a targeted region. This method enables the co-creators of energy retrofit projects to predict the decision-making behavior of business owners and their willingness to invest in energy retrofit based on their restaurants’ attributes in social media. As part of a current initiative, supported by the Pennsylvania Department of Environmental Protection, a series of design, development, and delivery of retrofit projects in the context of food services was closely studied. The initial focus was on the Philadelphia region as a model for broader statewide applications. Restaurant owners were surveyed, and information was collected about their buildings’ energy systems, utility bills, and their willingness and/or capacity to invest in energy efficiency upgrades. A “Philadelphia restaurants” dataset was then scraped from the Yelp website. The data includes information about restaurant ratings, number of reviewers, price range, working hours, and ambiance. A methodology was developed to analyze the data and to find patterns and criteria that could possibly influence the owner’s decision for investing in energy performance improvement. To identify patterns, two methods were employed: (1) several focus groups over program period with team members who played roles in project development phase and (2) statistical analysis to identify the factors contributing to successful projects. Using these screening questions from focus groups and the results from statistical analysis, the retrofit likelihood can be estimated for each individual project. This methodology can be applied to larger datasets and would enable the investigators to screen the whole market and prioritize the targets for business recruitment.
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