Evaluating the Uncertainty in Calculating Greenhouse Gas Emissions for Electricity Generation

Because 40% of U.S. CO2 emissions come from electricity generation and distribution, the ability to calculate CO2 emissions per unit of electricity consumed is crucial in order to perform a life-cycle analysis (LCA), be it of a product or process.  However, the greenhouse gas emissions associated with an individual entity’s electricity consumption is nearly impossible to calculate given the nature of electricity grids.  For this reason, LCA practitioners often employ emissions factors, or estimated average quantity of CO2 emitted per unit of energy consumed.  Unfortunately, emissions factors vary greatly both spatially and temporally due to different energy sources used for generation, as well as differing plant efficiencies. The authors point out that in addition to electricity coming from varying sources (for example, hydroelectric power provides much of the Pacific Northwests’s electricity due to the natural availability of that resource), electricity systems are quite complex because deregulation in the 1990s connected more remote customers with more remote generators, making it even more difficult to trace the source and associated greenhouse gas emissions of one’s electricity.  In this study Weber et al. (2010) calculated the variability in emissions factor estimates and demonstrated the uncertainty in using these estimates for LCA and policymaking.  The authors also made suggestions for how to deal with this uncertainty.—Lucy Block
Weber, C., Jaramillo, P., Marriott, J., and Samaras, C., 2010. Life Cycle Assessment and Grid Electricity: What Do We Know and What Can We Know? Environmental Science & Technology 44, 1895-1901.

          Christopher Weber, Paulina Jaramillo, Joe Marriott, and Constantine Samaras examine the uncertainty of emissions factors at various geographic levels of the U.S. and in different locales by collecting different emissions factors for CO2, SO2 and NOx (though CO2 contributes primarily to global warming and is thus the main focus of the paper).  The authors acknowledge that they did not take into account the emissions of upstream supply chains for electricity generation, noting that accounting for upstream emissions would only slightly increase uncertainty.  The authors calculated emissions factors along several potential regional delineations of the electric grid.  The emissions factor with the largest geographical area was the U.S. continental average (0.69 kg CO2/kWh), followed by three regions based on electrical grid connectivity—the Eastern, Western, and Texas Interconnects.  At a smaller level, Weber et al. used the 24 subregional grid delineations as defined by the EPA’s eGrid and used in the Greenhouse Gas Protocol, a tool for conducting LCAs.  Finally, the authors used data collected by the U.S. Energy Information Administration through voluntary greenhouse gas reporting since 1992.  The different datasets considered form seven independent estimates of electricity emission factors for every combination of U.S. state, eGrid subregion, and grid operator (whether independent system operators or regional transmission operators). 
For their dataset, the authors calculated a coefficient of variation (COV), or the normalized standard deviation.  A higher COV meant more variation between different estimates for electricity emissions factor, and therefore a higher uncertainty of amount of CO2 emitted per unit of electricity generation in the region.  The average CO2, COV for all delineations, or districts, considered out of 101 total was 0.19 (an average uncertainty of ±40% at two standard deviations) and ranged from a maximum of 0.70 to a minimum of 0.08.  The districts with highest associated uncertainty were those that had smaller or larger than average local or regional emissions factors.  Since electricity grids do not correlate closely with state borders, emissions factors estimated along state lines had higher variation than those estimated according to eGrid delineations. 
The authors conclude that LCA practitioners and policymakers generally do not have access to the data required in order to calculate a specific consumer’s electricity-related greenhouse gas emissions.  Therefore, for practical purposes, Weber et al. recommend that standards organizations provide clear guidelines for conducting LCA calculations, and by standardizing these calculations reduce overall comparative uncertainty between different LCAs.  The authors suggest that standards organizations should discourage the use of political borders in calculating emissions intensity for a particular area, as this unnecessarily increases uncertainty.  Furthermore, researchers should report kWhs consumed alongside the assumed grid emissions factor within an appropriate electricity system delineation, in order to increase transparency and allow for normalized comparisons of a specific product.  If estimating indirect CO2 emissions is required, Weber et al. suggest that researchers provide a range for the emissions factor.  In that case, if an entity wants to guarantee an emissions reduction or carbon neutrality, it can use the highest range of emissions factors. 
In public policy decisions, choosing a set of emissions factors will raise issues of equity.  If too general a set of emissions were to be used and an emissions trading market were to be set up, local distribution companies buying lower-carbon electricity would obtain an advantage, and local distribution companies buying higher-carbon electricity would be at a disadvantage.  Additionally, using more locally specific emissions factors could potentially penalize energy users in areas that have higher-carbon electricity simply due to natural resources.  For example, electricity in the Pacific Northwest will be lower-carbon because of the regional hydroelectric resources.  An industry located in the Pacific Northwest stands to lose less from policies to reduce carbon emissions than industries in other regions. 
The authors note that while it may be possible, depending on required level of accuracy for the investigation, to choose an appropriate emissions factor (e.g., if an industry operates in many locales throughout the country and the investigation does not require a particularly high level of accuracy in emissions calculations, one could use the national average emissions factor), consistency in calculating the indirect emissions of electricity consumption is of highest importance, along with transparency and reproducibility of methods.  

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