Abstract
Despite extensive empirical evidence of the environmental benefits of green buildings and the increasing urgency to reduce carbon emissions in cities, there has been limited widespread adoption of energy retrofit investments in existing buildings. In this paper, we empirically model financial returns to energy retrofit investments for more than 3600 multifamily and commercial buildings in New York City, using a comprehensive database of energy audits and renovation work extracted from city records using a natural language processing algorithm. Based on auditor cost and savings estimates, the median internal rate of return for adopted energy conservation measures is 21% for multifamily buildings and 25% for office properties. Logistic regression modeling demonstrates adoption rates are higher for office buildings than multifamily, and in both cases adopter buildings tend to be larger, higher value, and less energy efficient prior to retrofit implementation. The economically significant magnitudes of returns to adopted energy conservation measures raise important questions about why many property owners choose not to adopt. As such, we discuss incentive and regulatory mechanisms that can overcome financial and informational barriers to the adoption of energy efficiency measures.
Original language | English (US) |
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Article number | 118048 |
Journal | Applied Energy |
Volume | 306 |
DOIs | |
State | Published - Jan 15 2022 |
Keywords
- Building energy
- Energy efficiency
- Financial analysis
- Machine learning
- Retrofit investment
ASJC Scopus subject areas
- Mechanical Engineering
- General Energy
- Management, Monitoring, Policy and Law
- Building and Construction
- Renewable Energy, Sustainability and the Environment