Gale A. Boyd

Estimating the linkage between energy efficiency and productivity

Abstract Many analyses have referred to industrial productivity benefits associated with energy efficiency that are at least as great or larger than the energy benefits. If plants with higher energy intensity also tend to have lower productivity, then energy policy needs to consider this. This study examines this issue for two segments of the glass industry, using plant level data from the Census Bureau. Productivity is defined by the difference in `best practice’ production efficiency, as measured by data envelopment analysis (DEA). This study uses regression analysis to estimate how differences in plant level electricity and fossil fuel intensity, i.e. energy use per unit of production, are attributable to differences in plant level productivity and other economic variables, like energy prices and cumulative production. In every case, productivity differences between plants are statistically significant in explaining differences in plant energy intensity. The coefficient that links productivity to energy efficiency yields a less than proportional impact for only one industry and fuel type. For others the relationship is implies that a 1% increase in productivity increase energy efficiency by more than 1%. This effect is statistically significant for flat glass, but not container glass. The estimates of the price and learning-by-doing coefficients seem reasonable, but without imposing the assumption that all plants in the industry are equally productive. This lends further credence to a significant relationship between productivity and energy efficiency.

Decomposition of changes in energy intensity: A comparison of the Divisia index and other methods☆

Abstract The desirability of separating the effects of underlying shifts in the composition of the economy from changes in energy use patterns has been recognized by many researchers. The similarity between this decomposition of aggregate measures of energy intensity into its component parts and decomposition of aggregate output (cost) data into price and quantity indices is less well known. This paper makes some comparisons between energy intensity decomposition and the formulation of economic indices. We illustrate the useful properties of one particular index, the Divisia index in performing energy intensity decomposition.

Plant Level Productivity, Efficiency, and Environmental Performance of the Container Glass Industry

This paper presents a methodology and empirical results based on theMalmquist productivity index. We measure productivity while treatingpollution as an undesirable output. Our estimates show that technicalchange has contributed to productivity and environmental performancegrowth in the container glass industry, an energy and pollution intensivesector. Changes in inter-plant efficiency over time have made thisproductivity growth more rapid than otherwise would have occurred withthe underlying technical change. The efficiency estimates show that thereare both opportunities to improve productivity and reduce pollution in thisindustry, as well as productivity losses associated with the emissionscontrol. The shadow prices for NOx, the undesirable output we analyze,is quite high compared to other regulated sectors.

Multi-agent power market simulation using EMCAS

Countries around the world continue to restructure their electricity markets and open them up to competition and private investors in pursuit of economic efficiency and new capital investment. However, the recent volatility exhibited by many restructured power markets, in combination with several prominent market failures, have highlighted the need for a better understanding of the complex interactions between the various market participants and the emerging overall market behavior. Advanced modeling approaches are needed that simulate the behavior of electricity markets over time and model how market participants may act and react to changes in the underlying economic, financial, and regulatory environments. This is particularly useful for developing sound market rules that will allow these markets to function properly. A new and promising approach is to model electricity markets as complex adaptive systems using an agent-based modeling and simulation approach, such as is implemented in the electricity market complex adaptive system (EMCAS) software. EMCAS provides an agent-based framework to capture and investigate the complex interactions between the physical infrastructures and the economic behavior of market participants that are a trademark of the newly emerging markets. This paper describes the EMCAS agents, their interactions, the unique insights obtained from agent-based models, and discusses current model applications in several U.S., Asian, and European markets.

A Method for Measuring the Efficiency Gap between Average and Best Practice Energy Use: The ENERGY STAR Industrial Energy Performance Indicator

Summary A common feature distinguishing between parametric/statistical models and engineering economics models is that engineering models explicitly represent best practice technologies, whereas parametric/statistical models are typically based on average practice. Measures of energy intensity based on average practice are of little use in corporate management of energy use or for public policy goal setting. In the context of companyor plant-level indicators, it is more useful to have a measure of energy intensity that is capable of indicating where a company or plant lies within a distribution of performance. In other words, is the performance close to (or far from) the industry best practice? This article presents a parametric/statistical approach that can be used to measure best practice, thereby providing a measure of the difference, or “efficiency gap,” at a plant, company, or overall industry level. The approach requires plant-level data and applies a stochastic frontier regression analysis used by the ENERGY STARTM industrial energy performance indicator (EPI) to energy intensity. Stochastic frontier regression analysis separates energy intensity into three components: systematic effects, inefficiency, and statistical (random) error. The article outlines the method and gives examples of EPI analysis conducted for two industries, breweries and motor vehicle assembly. In the EPI developed with the stochastic frontier regression for the auto industry, the industry median “efficiency gap” was around 27%.

Estimating bounds on the economy-wide effects of the CEF policy scenarios

Abstract The Scenarios for a Clean Energy Future study relied primarily on “bottom-up” technology-based methods to estimate costs associated with its scenarios. These methods, however, do not allow for calculation of economy-wide or general equilibrium effects of the policies considered. We propose and apply a means of combining the bottom-up estimates with estimates of the costs associated with a carbon charge obtained from computable general equilibrium models. Our approach is based on the concept of production inefficiency: the economy lies within its production frontier with respect to the provision of energy services. The CEF technology policies are interpreted as moving the economy toward its frontier as well as moving the frontier outward, while the carbon charge induces a substitution effect along the frontier. This perspective allows a synthesis of the two sets of calculations.

FACTOR INTENSITY AND SITE GEOLOGY AS DETERMINANTS OF RETURNS TO SCALE IN COAL MINING

Increasing returns to scale (RTS) is frequently po stulated as affecting productivity in surface coal mining. However, it is not cl ear whether increased capital intensity or increased output is the relevant phen omenon. A ray-homothetic production function that incorporates the capital labor mix and fixed site geology into the scale elasticity is presented and estimated with a micro (mine level)dataset. The results indicate that higher capital int ensity contributes to higher RTS for some types of capital equipment, but not al l. On the average, increasing RTS was found, with few mines approaching optimal scale. Copyright 1987 by MIT Press.

Climate change and US energy policy

We present an analysis of the ability of the US to achieve significant reductions in CO2 emissions in the future. The emission-reduction objectives are 20% by the year 2000 and 50% by 2010, measured relative to 1985 levels. The economic sectors studied are electricity supply, industrial manufacturing, and transportation. The near-term reductions are considered to be achievable but with significant disruptions; the long-term goals are unlikely to be achieved without new breakthroughs in technology. Electricity-supply options, such as increased use of NG and more-efficient technologies, cannot alone allow us to achieve the goals, and end-use conservation will likely be the major contributor. Policy intervention in the industrial sector could achieve significant emission reductions, but concerns about international competition are important. In the transportation sector, analysis shows that fuel-economy regulation is preferable to gasoline-price increases.

3.4. Set of models

The use of disparate and detailed engineering and economic models is often necessary for environmental policy analysis and forecasting. However, to conduct consistent forecasting and policy analysis over all economic sectors these disparate models must be coordinated into a consistent model set. One approach to such a modeling effort is illustrated by the NAPAP Integrated Model Set, a collection of engineering, emissions-forecasting and energy-market models that is driven by and interacts with other energy-market and economic models.

Measuring Improvement in the Energy Performance of the U.S. Cement Industry

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.