Association for Public Policy Analysis & Management

Ambitious energy efficiency and carbon reduction goals across local, state, and federal governments have directed significant attention to the potential of retrofitting existing buildings to improve their energy performance. The U.S. buildings sector accounts for 40% of greenhouse gas emissions and primary energy use, a sizeable proportion that could be reduced by as much as a third using existing technologies and energy conservation measures (Brown et al. 2008). This is especially true for large buildings in cities; in New York City, for instance, buildings account for 79% of all carbon emissions and energy use and approximately half of this figure is attributable to buildings over 50,000 square feet (City of New York 2012; 2013; 2014).

However, the pace of adoption of energy efficient practices and technologies has been slow, and significant barriers persist to limit the market penetration of building energy improvements. Energy disclosure policies designed to catalyze market transformation have been introduced in over a dozen U.S. cities to provide a new stream of building energy data that can be used to overcome marketplace information asymmetries and knowledge gaps in building sustainability best practices and patterns of urban energy consumption (Burr et al. 2013; Kontokosta 2013, 2014). However, the challenge remains of transforming these data into useable insight that can shift decision-making and support evidenced-based outcomes.

It has been more than five years since the adoption of the first city energy disclosure policy, and the first benchmarking data derived from such a policy occurred in New York City for calendar year 2010. But have these policies had an impact on energy use and carbon emissions? This paper provides the first rigorous empirical examination of the effects of disclosure policies on building energy consumption and carbon emissions by analyzing matched energy data from 2010 to 2013. These data, from NYC’s Local Law 84 and the PLUTO land use database, contain four years of detailed information on energy use, occupancy and use characteristics, and other property attributes for over 15,000 large buildings. A normalized spatio-temporal index of energy performance is developed using predictive modeling that controls for building characteristics, climate, and spatial variables and accounts for time variant and invariant changes over the study period. The counterfactual scenario is established through the comparison of the normalized changes over time in energy use from buildings subject to the reporting mandate with a sample of propensity-score matched peer buildings from other cities that voluntarily reported energy data.

Given the rapid expansion of both mandatory building energy disclosure policies and voluntary self-reporting of energy data, this research is critical to the effective implementation of information disclosure initiatives to drive energy and carbon reductions. The results of this work provide needed insights into the efficacy of building energy disclosure policies as a component of urban sustainability and carbon reduction plans, and provides recommendations for evidence-based energy efficiency policy design, implementation, and evaluation.