Gale A. Boyd
  • Gale A. Boyd

  • Associate Research Professor of Economics
  • Economics
  • Gross Hall
  • Campus Box 90989
  • Secondary web page
  • Bio

    Gale Boyd is the Director of the Triangle Research Data Center (TRDC), a partnership between the U.S. Bureau of Census and Duke University in cooperation with the University of North Carolina, NC State, and Research Triangle Institute. The TRDC is one of 19 locations in the country where research on the confidential micro-data collected by the Federal Statistical System can be conducted. Dr. Boyd has almost 20 years of experience working with confidential data. His research applies frontier production function and data envelopment analysis approaches to industrial plant-level data for microeconomic modeling of industrial energy demand, emissions, and productivity. He is currently developing a series of industry specific energy efficiency benchmarking tools for use in the EPA Energy Star voluntary energy efficiency program. Prior to joining the Economics department at Duke, Gale was an economist and section leader of the Social Science, Policy and Law Section at Argonne National Laboratory.
  • Specialties

    • Environmental and Resource Economics
  • Working Papers

    • Gale Boyd and Charles McClure.
    • (Winter, 2012).
    • Waste, Water, and VOC Benchmarking in North American Automotive Manufacturing.
    • .
    • Gale A. Boyd and Gang Zhang.
    • (November, 2011).
    • Measuring Improvement in the Energy Performance of the U.S. Cement Industry.
    • .
    • (Nicholas Institute for Environmental Policy Solutions Report NI R 11-10, Duke University Durham, NC)
    • [web]
    Publication Description

    Recognizing the potential of energy efficiency to reduce CO2 emissions, the U.S. Environmental Protection Agency launched ENERGY STAR for Industry to educate manufacturers on steps to improve their energy efficiency. Energy management strategy is a key component of the ENERGY STAR approach. This paper focuses primarily on development of an updated ENERGY STAR industrial Energy Performance Indicator (EPI) for the Cement industry and the change in the energy performance of the industry observed when the benchmarking system was updated from the original benchmark year of 1997 to the new benchmark of 2008.

    • Gale Boyd, Tatyana Kuzmenko, Béla Személy, & Gang Zhang.
    • (April, 2011).
    • Preliminary Analysis Of The Distributions Of Carbon And Energy Intensity For 27 Energy Intensive Trade Exposed Industrial Sectors.
    • .
    • (Nicholas Institute for Environmental Policy Solutions: Duke Environmental Economics Working Paper Series: EE 11-03)
    • [web]
    Publication Description

    It is well documented that different manufacturing sectors require different amounts of energy. Primary materials conversion, e.g., iron ore and scrap into steel, limestone and sand into cement and glass, or wood and other fibers into paper, tend to be the most energy-intensive in the production process, while final consumer products like electronics and clothing require the least energy. This leads to something like the 80-20 rule, where a large portion of energy use is in a small number of industries. For example, the 2006 Manufacturing Energy Consumption Survey (MECS) reported that 75 percent of fuel use arises from only five of the 21 three-digit industries, using the North American Industry Classification System (NAICS). These five sectors are a small share of the total U.S. economy. The energy intensity for different industrial sectors is easily measured using published government statistics, but the plants within these industries are not homogeneous entities. This report measures the differences in energy use and associated CO2 emissions as a first step to understanding the within-sector heterogeneity of energy use.

    • G.A. Boyd.
    • (June, 2010).
    • Assessing Improvement in the Energy Efficiency of U.S. Auto Assembly Plants.
    • .
    • (Nicholas Institute for Environmental Policy Solutions: Duke Environmental Economics Working Paper Series: EE 10-01)
  • Research Summary

    Energy & Environment, Production Efficiency, Micro-data, index number decomposition
  • Current Projects

    Developing industry specific energy efficiency benchmarking tools for use in the EPA Energy Star voluntary energy efficiency program
  • Areas of Interest

    energy markets
    emission trading
    voluntary environmental programs
    climate change
  • Education

      • Ph.D.,
      • Economics,
      • Southern Illinois University,
      • 1984
  • Recent Publications

      • GA Boyd and EM Curtis.
      • (2014).
      • Evidence of an “Energy-Management Gap” in U.S. manufacturing: Spillovers from firm management practices to energy efficiency.
      • Journal of Environmental Economics and Management
      • ,
      • 68
      • (3)
      • ,
      • 463-479.
      • [web]
      • G.A. Boyd and Mark Curtis.
      • (Submitted, May, 2014).
      • Evidence of an “Energy-Management Gap” in U.S. Manufacturing: Spillovers from Firm Management Practices to Energy Efficiency.
      • Journal of Economics and Environmental Management
      • .
      Publication Description

      In this paper we merge a well-cited survey of firm management practices into confidential plant level U.S. Census manufacturing data to examine whether generic, i.e. non-energy specific, firm management practices, ”spillover” to enhance energy efficiency in the United States. For U.S. manufacturing plants we find this relationship to be more nuanced than prior research on UK plants. Most management techniques are shown to have beneficial spillovers to energy efficiency, but an emphasis on generic targets, conditional on other management practices, results in spillovers that increase energy intensity. Our specification controls for industry specific effects at a detailed 6-digit NAICS level and finds the relationship between management and energy use to be strongest for firms in energy intensive industries. We interpret the empirical result that generic management practices do not necessarily spillover to improved energy performance as evidence of an “energy management gap.”

      • GA Boyd.
      • (2014).
      • Estimating the changes in the distribution of energy efficiency in the U.S. automobile assembly industry.
      • Energy Economics
      • ,
      • 42
      • ,
      • 81-87.
      • [web]
      • G Boyd and Y Guo.
      • (March, 2014).
      • Energy performance indicator for integrated mills.
      • Paper360
      • ,
      • 9
      • (2)
      • ,
      • 26-29.
      Publication Description

      The US Environmental Protection Agency's Energy Star program and researchers at Duke University have worked with companies in the pulp, paper, and paperboard industry to develop the Energy Performance Indicator (EPI), a statistical model that lets integrated mills in the US compute their mill energy efficiency based on net demand for Total Source Energy per ton of product produced. Biomass generated at the plant is also not included in the net purchased energy accounting. The alternative is to account for the net energy consumption which presents several data challenges, so the system boundaries for the EPI are based on net energy demand. The statistical analysis finds that, while energy is needed for debarking and chipping, this process is also a net energy creator. By accounting for the type of woods used as an input to production, the EPI adjust for the availability of hog fuel when round wood is used by the plant versus the need to purchase more energy when chips are used.

      • G.A. Boyd and Walt Tunnessen.
      • (July, 2013).
      • Plant Energy Benchmarking: A Ten Year Retrospective of the ENERGY STAR Energy Performance Indicators (ES-EPI).
      • Proc. ACEEE Summer Study on Energy Efficiency in Industry
      • .
  • View All Publications
  • Teaching

    • ECON 325S.01
      • French Sci 2237
      • WF 03:05 PM-04:20 PM
    • ECON 690.99
      • Gross Hall 230E
      • M 01:25 PM-04:10 PM
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