DOE OSTI2010
Adoption of efficient end-use technologies is one of the key measures for reducing greenhouse gas (GHG) emissions. With the working of energy programs and policies on carbon regulation, how to effectively analyze and manage the costs associated with GHG reductions become extremely important for the industry and policy makers around the world. Energy-climate (EC) models are often used for analyzing the costs of reducing GHG emissions (e.g., carbon emission) for various emission-reduction measures, because an accurate estimation of these costs is critical for identifying and choosing optimal emission reduction measures, and for developing related policy options to accelerate market adoption and technology implementation. However, accuracies of assessing of GHG-emission reduction costs by taking into account the adoption of energy efficiency technologies will depend on how well these end-use technologies are represented in integrated assessment models (IAM) and other energy-climate models. In this report, we first conduct brief overview on different representations of end-use technologies (mitigation measures) in various energy-climate models, followed by problem statements, and a description of the basic concepts of quantifying the cost of conserved energy including integrating non-regrets options. A non-regrets option is defined as a GHG reduction option that is cost effective, without considering their additional benefits related to reducing GHG emissions. Based upon these, we develop information on costs of mitigation measures and technological change. These serve as the basis for collating the data on energy savings and costs for their future use in integrated assessment models. In addition to descriptions of the iron and steel making processes, and the mitigation measures identified in this study, the report includes tabulated databases on costs of measure implementation, energy savings, carbon-emission reduction, and lifetimes. The cost curve data on mitigation measures are available over time, which allows an estimation of technological change over a decade-long historical period. In particular, the report will describe new treatment of technological change in energy-climate modeling for this industry sector, i.e., assessing the changes in costs and energy-savings potentials via comparing 1994 and 2002 conservation supply curves. In this study, we compared the same set of mitigation measures for both 1994 and 2002 -- no additional mitigation measure for year 2002 was included due to unavailability of such data. Therefore, the estimated potentials in total energy savings and carbon reduction would most likely be more conservative for year 2002 in this study. Based upon the cost curves, the rate of change in the savings potential at a given cost can be evaluated and be used to estimate future rates of change that can be the input for energy-climate models. Through characterizing energy-efficiency technology costs and improvement potentials, we have developed and presented energy cost curves for energy efficiency measures applicable to the U.S. iron and steel industry for the years 1994 and 2002. The cost curves can change significantly under various scenarios: the baseline year, discount rate, energy intensity, production, industry structure (e.g., integrated versus secondary steel making and number of plants), efficiency (or mitigation) measures, share of iron and steel production to which the individual measures can be applied, and inclusion of other non-energy benefits. Inclusion of other non-energy benefits from implementing mitigation measures can reduce the costs of conserved energy significantly. In addition, costs of conserved energy (CCE) for individual mitigation measures increase with the increases in discount rates, resulting in a general increase in total cost of mitigation measures for implementation and operation with a higher discount rate. In 1994, integrated steel mills in the U.S. produced 55.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗