LBNL Report Number
Adoption of efficient end-use technologies is one of the key measures for reducing greenhouse gas (GHG) emissions. How to effectively analyze and manage the costs associated with GHG reductions becomes 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 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 a brief review of different representations of end-use technologies (mitigation measures) in various energy-climate models, followed by the problem statement, and a description of the basic concepts of quantifying the cost of conserved energy including integrating no-regrets options. According to IPCC (2001), no-regrets opportunities for GHG emissions reduction are the options whose benefits such as reduced energy costs and reduced emissions of local or regional pollutants equal or exceed their costs to society, excluding the benefits of avoided climate change. In this report, a no-regrets option is defined as a GHG reduction option (i.e., via energy efficiency measure) that is cost effective over the lifetime of the technology compared with a given energy price, without considering benefits of avoided climate change. There are two types of treatments of no-regrets options:
- options that include other benefits, e.g., reduced operational and maintenance costs and productivity benefits; and
- options that exclude other benefits. Although existence of no-regret options is not entirely acknowledged by some economists, a number of cost-effective measures in the U.S. cement sector were identified and studied in this report, regardless whether or not other benefits are included. There are many factors including market barriers and knowledge gap that contribute to slower adoption of such measures in the markets.
Based upon reviews of literature and technologies, 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 cement 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.
Through characterizing energy-efficiency technology costs and improvement potentials, we have developed and presented energy and carbon reduction cost curves for energy efficiency measures applicable to the U.S. cement industry for the years 1994 and 2004. The cost curves can change significantly under various scenarios: the baseline year, discount rate, energy intensity, cement production, industry structure (e.g., blended vs. non-blended cement making, wet kiln conversion to dry cement making), efficiency measures, share of cement production to which the individual measures can be applied, and inclusion of other non-energy benefits. Based upon limited data available for quantifying other benefits of individual mitigation measures, we have found that inclusion of other benefits from implementing some mitigation measures can change the actual costs of conserved energy. 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 this study, all the cost data (U.S. dollars) are obtained and presented in the currency values for the respective reference years (i.e., 1994, 2004). A direct comparison of costs (U.S. dollars), when desired, can be made by converting the existing reference-year data (i.e., 1994, 2004 in this study) to a preferred reference year (e.g., 2007). The conversions can be accomplished by multiplying the existing cost in a reference year by a Gross Domestic Product (GDP)-based inflation index for the preferred year (BEA 2009).
In this study, we included 31 mitigation measures for year 1994 and 36 mitigation measures for year 2004 in the analysis based upon availability of such data for each year, respectively. We also estimated potential energy savings and carbon-emission reduction corresponding to the mitigation measures for each year (1994 and 2004), respectively. In addition, we have developed and defined the concept for cost of carbon reduction (CCR) associated with the mitigation measures; therefore, the cost of carbon reduction for each mitigation measure can be established and estimated based upon available information. Main findings are included in the following.
We evaluated final energy use in the U.S. cement making sector, and estimated that 366 petajoules (PJ) final energy was used in 1994, and 465 PJ final energy was used in 2004. We calculated that from 1994 to 2004 the cement production energy intensity has decreased from 6 GJ/t to 5.1 GJ/t (a reduction of 15%) in wet-cement production, indicating efficiency technology uptakes in wet-cement production over the period of time. During the same period, the cement production energy intensity remained stable at the level of 4.5-4.6 GJ/t for dry-cement production, indicating no significance change in efficiency technology uptakes. In addition, there was a production expansion in less energy intensive dry-cement in 2004. As a result, the overall cement production energy intensity decreased from 4.9 GJ/t to 4.7 GJ/t (a reduction of 4%) from 1994 to 2004.
The potential savings of final energy use from applying 31 measures was 42 PJ for blended cement and 39 PJ for non-blended cement in 1994, while the potential savings of final energy use resulting from applying 36 mitigations measures was 54 PJ for blended cement and 72 PJ for non-blended cement in 2004. Therefore, the technical potential of energy savings was approximately 22% in 1994 and 27% in 2004.
We have identified a number of cost-effective mitigation measures in this study. Furthermore, inclusion of other benefits from implementing mitigation measures can reduce the costs of conserved energy significantly, making more measures cost-effective. We estimated that the potential savings of final energy use resulting from cost-effective mitigations measures was 53 PJ in 1994 and 89 PJ in 2004, corresponding to 15% and 19% of total annual final energy use in the U.S. cement industry in 1994 and 2004, respectively. Implementing cost effective measures can result in significant energy savings relative to the total annual energy use in the sector, and more even so when compared to the technical energy savings potential.
The total carbon emissions associated with the U.S. cement sector consists of two categories:
- energy use for cement production, and
- the direct emissions from cement-making processes.
We estimated that total carbon emissions from the cement sector in the U.S. were approximately 18.9 million ton of carbon (MtC) in 1994 and 24.2 MtC in 2004. We estimated that the potential reduction of carbon emissions resulting from the applicable mitigation measures was 4.2 MtC (2.2 MtC blended, and 2.0 MtC non-blended) in 1994 and 6.5 MtC (2.8 MtC blended, and 3.7 MtC non-blended) in 2004, corresponding to 22% and 27% of annual total carbon emissions in 1994 and 2004, respectively. We have found that applying cost-effective measures would reduce carbon emissions by 2.8 MtC in 1994 and by approximately 4.7 MtC in 2004, corresponding to 15% and 19% of annual total carbon emissions in 1994 and 2004, respectively. Implementing cost effective measures can result in significant carbon-emission reduction relative to the total carbon emissions in the sector, and more even so when compared to the technical potential in carbon-emission reduction.
We have also concluded that based upon the cost curves derived from available information on mitigation measures for both years, the rate of change in the energy-savings or carbon-reduction potential at a given cost can be evaluated and be used to estimate future rates of change for input in energy-climate models. Accuracies of such estimation of the rate change may be improved as more comprehensive information on characterizing the mitigation measures becomes available. Implementing existing cost effective measures can result in significant energy savings and carbon-emission reduction for both years relative to their technical potential in energy savings and carbon-emission reduction. In addition, total costs of conserved energy increase with the increases in discount rates. The outcomes from this research provide information on initial technology database that can be accessible to integrated assessment modeling groups seeking to enhance their empirical descriptions of technologies.
While many energy efficiency technologies have become cost-effective to mitigate long-term climate change, it is important and necessary to continue to incorporate new information on technology characteristics, and their evolution and response to energy and carbon price into various integrated assessment models to enhance empirical descriptions of the technologies, e.g., econometric models, service demand models, discrete choice models, or computational general equilibrium (CGE) models.
There appears to be a need to develop and refine sectoral algorithms and produce databases that can be used to match the needs of different integrated assessment modeling of climate policies. New algorithms should allow transformation of information on behavioral responses, technology costs, energy savings, other benefits, and policy costs into meaningful and functional data forms. Developing such algorithms may require customization and automation of database functions that would account for many variables. Furthermore, the desired data-model linking effort will require close interfaces between modelers and the developers of the cost-curve databases on energy efficiency measures. Future efforts should also include additional business sectors.