Gale A Boyd
  • Gale A Boyd

  • Director of the Triangle Research Data Center and Senior Research Scholar
  • Economics
  • Gross Hall
  • Campus Box 90989
  • Secondary web page
  • Bio

    Gale Boyd is the Director of the Triangle Census Research Data Center (TRDC), a partnership between the U.S. Bureau of Census and Duke University in cooperation with the University of North Carolina. The TRDC is one of nine locations in the country where research on the confidential micro-data collected by Census can be conducted. Dr. Boyd has over 15 years of experience working with confidential Census 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
  • Research Description

    Gale Boyd’s research interests pertain to production efficiency, micro-data, index number decomposition, and energy in relation to the environment. His studies have also focused on energy markets, developing industry, voluntary environmental programs, and emissions trading. In his recent studies, he analyzes his subjects through microeconomic modeling and proceeds to develop specific energy efficiency benchmarking tools. He has published his papers in numerous leading academic journals and volumes, from The Energy Journal and the Annual Review of Energy and Resources to the Journal of Economic Literature and the Journal of Industrial Ecology. His writings include such titles as, “Estimating Plant Level Manufacturing Energy Efficiency with Stochastic Frontier Regression,” “Modeling Industrial Energy Consumption,” and “Advances in Energy Forecasting Models based on Engineering-Economics.” Dr. Boyd’s current projects involve the investigation of energy efficiency benchmarking tools for use in the EPA Energy Star voluntary energy efficiency program.
  • 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

      • PhD,
      • Economics,
      • Southern Illinois University - Carbondale,
      • 1984
  • Recent Publications

      • Schneck, Joshua, and Gale Boyd.
      • (Submitted, December, 2012).
      • Distribution of Emissions Permits to the U.S. Pulp and Paper Sector under Alternative Output-Based Allocation Schemes.
      • Jornal of Environmental Management
      • .
      • (Nicholas Institute for Environmental Policy Solutions Report NI R 11-10, Duke University Durham, NC)
      • [web]
      Publication Description

      Under a cap-and-trade climate policy, emissions allowances—tradable rights to emit a fixed amount of greenhouse gases—become scarce and valuable resources that change the economic incentives to implement more energy-efficient processes and energy management practices, and to select fuels with lower carbon content. A key question accompanying the design of any such policy is how to allocate these allowances. This paper examines how key design elements and industry characteristics affect the distribution of allowances to U.S. pulp and paper firms under three variations of a proposed output-based allocation program—the American Power Act’s emissions allowance rebate program

      • G.A. Boyd.
      • (Submitted, December, 2012).
      • Estimating the Changes in the Distribution of Energy Efficiency in the U.S. Automobile Assembly Industry.
      • Energy Economics
      • .
      • (Nicholas Institute for Environmental Policy Solutions: Duke Environmental Economics Working Paper Series: EE 10-01)
      • Boyd, G. and G. Zhang.
      • (May, 2012).
      • Measuring improvement in energy efficiency of the US cement industry with the ENERGY STAR Energy Performance Indicator.
      • Energy Efficiency
      • ,
      • 1-12.
      • [web]
      Publication Description

      The lack of a system for benchmarking industrial plant energy efficiency represents a major obstacle to improving efficiency. While estimates are sometimes available for specific technologies, the efficiency of one plant versus another could only be captured by benchmarking the energy efficiency of the whole plant and not by looking at its components. This paper presents an approach used by ENERGY STAR to implement manufacturing plant energy benchmarking for the cement industry. Using plant-level data and statistical analysis, we control for factors that influence energy use that are not efficiency, per se. What remains is an estimate of the distribution of energy use that is not accounted for by these factors, i.e., intra-plant energy efficiency. By comparing two separate analyses conducted at different points in time, we can see how this distribution has changed. While aggregate data can be used to estimate an average rate of improvement in terms of total industry energy use and production, such an estimate would be misleading as it may give the impression that all plants have made the same improvements. The picture that emerges from our plant-level statistical analysis is more subtle; the most energy-intensive plants have closed or been completely replaced and poor performing plants have made efficiency gains, reducing the gap between themselves and the top performers, whom have changed only slightly. Our estimate is a 13 % change in total source energy, equivalent to an annual reduction of 5.4 billion/kg of energy-related carbon dioxide emissions.

      • G.A. Boyd.
      • (February 7, 2012).
      • A Statistical Approach to Plant-Level Energy Benchmarks and Baselines: The Energy Star Manufacturing-Plant Energy Performance Indicator.
      • Proc: Carbon Management Technology Conference
      • ,
      • 1
      • (150574)
      • ,
      • (pp. 1-14).
      • Orlando, Florida, USA:
      Publication Description

      Energy efficiency is a metric of relative performance, to either a benchmark or plant specific baseline. As such, these metrics are an integral component of energy and carbon management practices. This paper presents an approach used by Energy Star to implement manufacturing plant energy benchmarking, or Energy Performance Indicators (EPI), for a variety of industries. To date, EPI have been developed for industries ranging from “light” manufacturing such as auto assembly and food processing to “heavy”, energy intensive sectors like cement, glass, and paper. Since “all plants are different,” the EPI control statistically for differences between plants including product mix, climate, utilization, and vertical integration. After adjusting for differences between plants the EPI statistically “scores” plants from 1 to 100 in terms of their percentile ranking. When characteristics change within a plant over time, these EPI can also be used to construct adjusted energy baselines. This paper describes the approach used for developing the EPI. It compares the results for 10 industries, in terms of the types of variables that are included and the range of performance, measured by the inter-quartile range. The paper gives an example from pharmaceuticals of how the EPI can be applied to create adjusted baselines, in this case normalizing for year to year differences in weather. The paper provides examples of how the distribution of performance has changed over time for auto assembly and cement manufacturing. The range of performance for both sectors has narrowed, contributing to an industry wide reduction in energy and carbon. Since Energy Star for Industry was launched, the number of EPI in use or under development has grown to 19 sectors within 11 industries. Until now, no industry-wide, plant-level, energy benchmark previously existed for these industries. We find that every industry has plant specific factors that influence energy consumption, so that a measure of energy efficiency must account (normalize) for those differences to be a useful management tool.

      • G.A. Boyd and Yifang Guo.
      • (Submitted, 2012).
      • Development of Energy Star® Energy Performance Indicators for Pulp, Paper, and Paperboard Mills.
      • Ecological Economics
      • .
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  • Teaching

    • ECON 325S.01
      • Perkins 2060
      • WF 03:05 PM-04:20 PM
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