Estimating Jobs from Building Energy Efficiency

  • April 30, 2009
  • COWS
  • Eric Sundquist

What numbers and kinds of jobs result from investment in building energy efficiency (EE)? This report, based on joint work by COWS (University of Wisconsin-Madison) and the Powell Center for Construction and Environment (University of Florida), suggests a way to get at least policylevel answers to this question for state and local programs. Program designers, armed with better knowledge of the building stock, energy costs, specific EE measures likely to be supported, and other local data, will be able to provide much more robust estimates as they move toward program implementation. The hope here is simply to get them started.

To define our scope at the outset, we treat the question above deliberately narrowly — limiting ourselves to jobs in direct installation of EE measures. This excludes indirect “upstream” jobs in the production and sale of the many parts, equipment, and materials used in those measures (hereinafter, “materials”); “downstream” jobs in the disposal or recycling of replaced materials; jobs induced by demand from new income; and any jobs resulting from EE’s increase of property value and building occupant health and productivity. We define job numbers for installers not by actual positions, many of which are part-time, but “job-years” (2080 hours) of work. We distinguish job “kind” only by broad skill levels in the work involved (not getting without getting into the maze of vocational training that more advanced ones require), which we assume to be constant across all EE measure categories, and those categories themselves. So, for example, we say how many people in HVAC will be needed, but not how many steamfitters vs. sheetmetal workers. Finally, we report only EE measures that achieve simple cost-effectiveness (benefits greater than cost) in ≤ 10 years.

Within this scope and set of conventions, this report identifies the sorts of EE measures applicable to different building types and the sorts (and cost) of labor needed to apply those measures. It then puts these two together to estimate answers to the question asked above, on different assumptions of project focus and labor cost. Given wide variation in the EE universe across and within building types, and variation in labor cost across and within regions, the exercise has more than the usual limits of reporting means and modes. But it does identify the sorts of questions that should be asked here (which buildings, what measures, what skills requirements and associated labor costs) and some ballpark answers, providing at least a starting point for more detailed local investigations.