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land fi ll, which is the total amount of carbon in the product remaining after partial decay. The total C stored is then converted to CO 2 -equivalents and subtracted from the total CO 2 -eq emissions to report GHG emissions, which is equivalent to the CO 2 originally sequestered from the atmosphere by the biogenic source minus the C-equivalent that decays in the land fi ll. Since the CO 2 originally sequestered was accounted for the forestry stage of the LCA model, the carbon storage in CO 2 equivalents was not subtracted from the total CO 2 emissions, to avoid double-counting. WARM by default does not track biogenic CO 2 emissions from land fi lling or combus- tion processes. These were then added in the model. The C content of the fi nal product was assumed to be the same as C content of biomass. The percent of material combusted to CO 2 was 98%, the same percentage used in combustion of other materials in WARM. Because land fi ll gas is on average 50% CO 2 and50%CH 4 (Ingwersen et al., 2015), the amount of CO 2 emission was set to match the amount of CH 4 emissions. National average conditions and other default choices for WARM were used for model parameters. 2.1.6. Background data For all processes, production of generic chemicals, industrial water and wastewater treatment, and fuels other than petroleum are represented by data from Ecoinvent v2.2 (ecoinvent Centre, 2010). Data for petroleum fuels was taken from the inventories described in Sengupta et al. (2015b). Data on crude oil and natural gas extraction as well as data from general forestry operations were taken from the USLCI Database (NREL, 2013). 2.1.7. Data quality Data used in this study scores high for quality based on in- dicators like completeness, representativeness, consistency, reproducibility, data sources, technology coverage, precision, geographical coverage, time-related coverage, and uncertainty (ISO, 2006). The scoring approach is based on Weidema and Wesnaes (1996); a semi-qualitative matrix pedigree method. The indicators of completeness, time-related coverage (temporal cor- relation), geographical coverage (geographical correlation), and technology coverage (technical correlation) match the indicators in ISO 14044. Sample size compliments the completeness indicator. Temporal, geographical, and technical correlation describes repre- sentativeness. Consistency, reproducibility, and data sources are discussed throughout this section, Materials and methods. Uncer- tainty is addressed with a sensitivity analysis. See Table A3 for the various data quality scores. 2.1.8. Sensitivity analysis Four types of scenarios were analyzed to understand the importance of new methods and datasets, as well as to evaluate inherent data uncertainties. One scenario was developed to understand the life cycle result differences brought about by the new IPSA approach. In this sce- nario, a facility-level mass allocation approach was used to estimate the paper facility inventory to contrast with the new IPSA line level estimations. Two scenarios were created explicitly to test how the speci fi c datasets for pulp and regional electricity compared with national average data. In the fi rst of these scenarios, US national average pulp data from 2011 was used to create a modi fi ed pulp LCI re fl ective of national conditions. Data from this scenario were taken from a dataset developed by the US EPA and others to support the Universal Industrial Sectors Integrated Solutions Model for the pulp and paper sector (US EPA, 2014a, 2014b; Modak et al., 2015). The national average energy inputs, and GHG and criteria pollutant emissions developed from these data, are presented in the Appendix Table A5. Other input process names and inputs were set
VOCs) were adopted from Cai et al. (2012), which are the same used in the GREET model. Fuel source-speci fi c water loss estimates were included based on Macknick et al. (2012). Electricity processes by fuel source were then included as inputs into regional and/or na- tional level electricity generation mix processes. Regional US elec- tricity mixes by fuel source came from the EPA eGRID year 2010 data (EPA, 2014). Outside the US, national level mixes came from international energy statistics available from the Energy Informa- tion Administration (EIA, 2015). Table A1 in the Appendix sum- marizes the power mixes assumed for the facilities. Electricity generation mix processes were then connected to ‘ electricity, at industrial user ’ processes for modeling distribution to point of use. National level losses associated with distribution were estimated based on Schmidt et al. (2015). No emissions or infra- structure demands were modeled for distribution, assuming the insigni fi cance of impacts associated with distribution (aside from losses) relative to production and other upstream processes. 2.1.4. Packaging Materials and processes used to model packaging for a roll of paper towels comes from Ecoinvent v2.2 (ecoinvent Centre, 2010). This includes folding boxboard for the paper towel core and poly- ethylene (LLDPE) for the plastic wrapper of the roll. P & Gprovided the mass of the core and wrapper. No secondary and transport packaging materials included. The contribution of packaging to the results is low ( < 1%) it is not included in the tables and graphs of the analysis. 2.1.5. Distribution, use, and end-of-life The Bounty towels made at the two production facilities are distributed by tractor-trailer truck to a mix of distribution centers, clubs stores, and retailers across North America. Average distances range from 300 to 500 miles one-way and a load factor of 0.85 (representing trucks at 50% load capacity to pick up goods and at 100% capacity delivering goods) was assumed. No burdens were allocated to the retailer or the consumer to store, display, or use the product. The product does not contain chemicals that volatilize or leach, so there were no emissions to report during the use phase. The roll of paper towels was assumed to be used for common household purposes, disposed, and hauled off with other house- hold garbage to either a Municipal Solid Waste (MSW) land fi ll or incineration facility. US national average end of life treatment for the 2011 study year (US EPA, 2014a, 2014b) statistics provided the mix of land fi ll and incineration. Since used towels cannot be recycled because the fi bers are two short to be commercially viable as a paper stock, the following equation estimates the percentage of towel waste to land fi ll:
l a 1 r a
(1)
l ¼
where l a is the reported average percentage of US MSW to land fi ll and r a is the reported average percentage recovered via recycling. The remaining percentage was assumed to be incinerated. GHG emissions and energy use related to end-of-life treatment were modeled with the EPA Waste Reduction Model (WARM) (US EPA, 2015a, 2015b, 2015c). The openLCA database of WARM (Ingwersen et al., 2015) was used instead of the EXCEL ® version of WARM to align WARM modeling choices of biogenic carbon with the approach taken in this study. In the model, each material was treated inde- pendently (towel, core, and wrapper). As the speci fi c materials are not available in WARM, proxy materials were chosen for modeling. ‘ Mixed paper (primarily residential) ’ represented the towel, ‘ corru- gated cardboard ’ represented the core, and ‘ LDPE ’ the plastic fi lm wrapping. WARM by default accounts for “ carbon storage ” in the
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