Evaluating the Results

CENTRAL CHALLENGE: How can Yale evaluate the results of the carbon charge pilot? How can it determine the drivers behind emissions reductions? How can it assess which scheme was most effective?

Evaluation of the pilot was based on semi-structured interviews, focus group discussions, survey data and metered energy use. The four schemes were assessed based on three criteria: administrative feasibility (including technical and political), effectiveness (whether the carbon charge increased understanding, motivation, and action for reducing energy use), and impact (whether the carbon charge reduced emissions).

The treatment groups (20 pilot buildings) performed better than the control groups (the 280 other Yale buildings) across the board. However, participants cited many reasons for their energy reductions. Some said that the price signal and energy report directed their attention to energy use and carbon emissions. Others stated that the net financial incentive was motivating and they wanted to win a rebate. Then, there were individuals who reported that the financial incentive had no effect on their motivation. Rather, they felt personally responsible for managing their building's energy use because their unit head had nominated them. It is probably a combination and compounding of these factors, including the price signal, information provided by the energy report, and the engagement from the Project Team, fellow participants, and other stakeholders in the carbon charge, that motivated participants to take energy action. Still, more work is needed to evaluate the relative importance of each factor in driving impacts.

Discussion Questions

• What are the drivers behind emissions reductions across the pilot units? How can Yale better structure its approach to determine this? Could the results from the Yale's carbon charge pilot show a false positive?
• While the treatment groups achieved more emissions reductions than the control group, the results were not statistically significant, how can Yale design future pilots to collect better quantitative data for statistical analysis?