Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 357
Electricity from Renewable Resources: Status, Prospects, and Impediments E Attributes of Life-Cycle Assessment While it is the intention of all life-cycle assessments (LCAs) to cover technologies from “cradle to grave” in a systematic way, there is significant variability in the assumptions, boundaries, and methodologies used in these assessments. Therefore, comparisons of LCAs should be done with caution; each is an approximation of a technology’s actual impact. A major complication in comparing LCAs is that there is no set standard by which such analyses are carried out. There are two basic kinds of LCAs: economic input/output (EIO) and process analysis (PA). PA can be thought of as a “bottom-up” approach in which specific information for the energy and emissions associated with each component of the technology is determined and then combined to obtain a complete life-cycle impact. In addition to being very time intensive, the PA can be limited in its utility by the fact that there is often a lack of information concerning one or more components of the technology, and this can lead to truncation errors. EIO, on the other hand, does not track individual components, but instead uses economy sector level data to quantify the relationship between energy and the materials and processes produced. As compared to PA, EIO can be thought of as being a “top-down” approach. While having the advantage of being less time and data intensive, the EIO is limited in its accuracy by its dependence on highly aggregated data that may or may not be appropriate for the specific process or material being considered. Recent investigations suggest that EIO analyses tend to give higher energy use and emissions estimates than does PA, perhaps because PA is only able to consider the subset of processes for which data are available (Fthenakis and Kim, 2007; Odeh and Cockerill, 2008). A third method, sometimes referred to as
OCR for page 358
Electricity from Renewable Resources: Status, Prospects, and Impediments the “hybrid” LCA, attempts to address this issue by combining aspects of both techniques: using the PA approach where specific data are available and the EIO method where such data are not available. To provide a coherent and consistent framework for LCAs, various agencies have compiled life-cycle inventory (LCI) databases that list the materials and energy inputs required for various technologies. For example, ECLIPSE (Frankl et al., 2004) is a LCI assessment project developed in Europe that looks at emissions and resource consumption. Other PA-type LCI databases include DEAM (Ecobilan, 2001), Franklin (Franklin Associates, 2008), and Ecoinvent (Pre Consultants, 2007b). EIO analyses typically rely on economic databases that are compiled by governmental bodies. For example, in the United States, the Department of Commerce generates data on air emission and water and energy use for 485 commodity sectors from various data sources. Beyond the choice of LCA method and LCI database, there are any number of other factors that can affect LCA results and cause discrepancies among analyses. Assumptions about power plant capacity (or lifetime output), plant life expectancy, and energy infrastructure influence LCA results. In general, when comparing installations of the same energy technology, those with longer plant life expectancies and greater electrical output to the grid will have lower lifetime emissions per unit of electricity. The nature of the underlying energy structure that supports the manufacture, operation, and dismantling of a given facility are also quite important. For example, the construction of a wind turbine in Sweden where much of the energy is produced using renewable sources will generally have less embedded CO2 emissions than the same turbine produced in the United States, where coal-fired power plants generate a significant fraction of the electricity available on the grid. A further source of discrepancies for LCAs arises from the fact that these assessments are aimed at technologies that are often undergoing continuous modification and improvement. Comparisons cited here of an LCA for a given technology may differ because the technology under consideration evolved over the period from one LCA to another. This is especially true for solar and wind technologies where the ongoing rate of innovations is quite rapid. A further complication arises from the fact that some LCAs assess impacts for a hypothetical, future installation of the technology. Another shortcoming of LCA is that it addresses only a single environmental impact. If one is in the position of choosing one technology over another, it would be desirable to have a more integrated understanding of the overall environmental
OCR for page 359
Electricity from Renewable Resources: Status, Prospects, and Impediments impact of one technology over another. Environmental valuation methods attempt to do this by estimating the overall ecosystem impact of a technology using currency or “willingness to pay” as the unit to integrate across types of impacts. Impact categories included in environmental valuation methods typically relate to damages to humans, ecosystems, and resources. ExternE (European Commission, 1997) and Eco-indicator 99 (Pre Consultants, 2007a) are examples of environmental valuation methods used in Europe. The main criticism of environmental valuation methods is of the step where disparate effects of LCAs are weighted and normalized into a single value per technology. Often the development of a single value is not adequate to capture the complexities of a technology, and metrics like “willingness to pay” can vary over time. For this reason we limit our discussion to LCA results. REFERENCES Ecobilan, P. 2001. TEAM/DEAM. 2001. Bethesda, Md.: PriceWaterhouseCoopers. European Commission. 1997. External Costs of Electricity Generation in Greece. ExternE Project. Brussels. Frankl, P., Corrado, A., and S. Lombardelli. 2004. Photovoltaic (PV) Systems. Final Report. ECLIPSE (Environmental and Ecological Life Cycle Inventories for present and future Power Systems in Europe). Brussels: European Commission. January. Franklin Associates. 2008. Available at http://www.fal.com/lifecycle.htm. Fthenakis, V.M., and H.C. Kim. 2007. Greenhouse-gas emissions from solar-electric and nuclear power: A life-cycle study. Energy Policy 35:2549-2557. Odeh, N.A., and T.T. Cockerill. 2008. Life cycle GHG assessment of fossil fuel power plants with carbon capture and storage. Energy Policy 38:367-380. Pre Consultants. 2007a. Available at http://www.pre.nl/eco-indicator99. Pre Consultants. 2007b. Available at http://www.pre.nl/ecoinvent/.
OCR for page 360
Electricity from Renewable Resources: Status, Prospects, and Impediments This page intentionally left blank.