Author response to second letter to the editor, J. Air Waste Manage. Assoc. 65: 245–246

Dear Editor,

I appreciate the opportunity to respond to a second letter by Gurney and colleagues (Gurney et al., 2015). As before (Gurney et al., 2014), they disagree with my conclusion (Quick, 2014a) that the annual CO₂ emission tallies for 210 U.S. power plants were more accurately calculated from coal consumption and quality (Energy Information Administration [EIA], 2012) than flue gas flow and composition (Clean Air Markets Division [CAMD], 2012). Their second letter repeats many of their earlier comments; my response is limited to their new complaints.

Camd Measurement Error

Random error inevitably increases the range of measurement variation. This is true regardless of whether one power plant is measured multiple times, or multiple power plants are measured one time (see Supplemental Materials). Moreover, where results from two measurement methods differ due to random error, these differences will be normally distributed around a mean near zero (Hutcheon et al., 2010). However, even though the differences between the EIA and CAMD emission rates are normally distributed around a mean near zero, and the CAMD emission rates show a greater range of variation than the EIA emission rates, Gurney et al. (2015) argue that “The greater CAMD variance could just as easily be explained by the CAMD data perfectly reflecting the true emissions and the EIA randomly varying about that true emission amount.”

Li and Gurney (2015) support this argument with a mathematical model that correctly shows that this is possible by assuming perfect CAMD measurements where the EIA measurement variation is entirely random. However, if the EIA measurement variation is entirely random, then the significant correlation (P < 0.001, R² = 0.61) between the EIA and CAMD emission rates shown in Figure 3 of Quick (2014a) must be the result of random chance. Although this is possible, the P-value indicates that the probability that the CAMD data show the true emission rates is less than 0.1%.

Gurney et al. (2015) also state: “If there were many repeat measurements at a single power plant from each of the CAMD and EIA data sets, we might be persuaded to Quick’s position on measurement error.” Although I doubt it would be possible to repeatedly measure an annual CO₂ emission tally, it is possible to use the available data to further demonstrate random CAMD measurement error. Knowing that random error is reduced by multiple measurements, it follows that random CAMD measurement error will be reduced at plants with multiple emission measurement systems. Accordingly, we should observe better agreement between the annual EIA and CAMD CO₂ emission tallies for plants with multiple continuous emission monitoring systems (CEMS) compared to plants with just one. Observations shown in Figure 1 confirm this prediction.

Figure 1. Consistent with random CAMD measurement error, better agreement between CAMD and EIA CO₂ emission tallies is observed for plants with multiple Continuous Emission Monitoring Systems (CEMS) compared to plants with just one. Data are for 173 U.S. coal-fired power plants during 2009, where n is the number of power plants, sd is the standard deviation, and IQR is the inter-quartile range. The percent difference was calculated according to: 100 x (CAMD – EIA)/[(CAMD + EIA)/2].

Eia Measurement Error

Gurney et al. (2015) again question the reliability of the data that I used to calculate the EIA measurement uncertainty, but, as before, do not provide any new information that might challenge my calculation and conclusion. For example, had Gurney and colleagues found better data that showed a shipment weight error of ±5.0% (rather than ±0.25%) and a stockpile measurement error of ±15% (rather than ±5.0%), I could have improved my calculation to show an average ±2.7% minimum error for the annual EIA CO₂ emission tallies. Rounding this minimum error up to ±3.0% would indicate a CAMD measurement error of ±10.4%, instead of ±10.6% shown by eq 1 of Quick (2014b). Note that despite these large imaginary increases in the EIA measurement errors, the resulting CAMD measurement error is largely unchanged. Moreover, this robustly calculated result is consistent with Figure 3 of Quick (2014a), which also shows a large CAMD measurement error. It is also consistent with the greater range of CAMD measurement variation shown in Figure 2 of Quick (2014a), which is an inevitable result of random measurement error that is further demonstrated in Figure 1. Conversely, there is no evidence to support a large EIA measurement error.

Sincerely,Jeffrey C. Quick

Utah Geological Survey

Supplemental Materials

Supplemental data for this response can be accessed at http://dx.doi.org/10.1080/10962247.2015.1006098